Reading Bigger Data Files using read.csv() Command.

d2 = read.csv(file.choose())
d2

Getting information of an Object using summary() command.

summary(d2)
##      Name               Total              HP             Attack      
##  Length:1045        Min.   : 175.0   Min.   :  1.00   Min.   :  5.00  
##  Class :character   1st Qu.: 330.0   1st Qu.: 50.00   1st Qu.: 55.00  
##  Mode  :character   Median : 458.0   Median : 68.00   Median : 77.00  
##                     Mean   : 439.3   Mean   : 70.07   Mean   : 80.47  
##                     3rd Qu.: 515.0   3rd Qu.: 82.00   3rd Qu.:100.00  
##                     Max.   :1125.0   Max.   :255.00   Max.   :190.00  
##     Defence         Sp_attack        Sp_defence         Speed       
##  Min.   :  5.00   Min.   : 10.00   Min.   : 20.00   Min.   :  5.00  
##  1st Qu.: 50.00   1st Qu.: 50.00   1st Qu.: 50.00   1st Qu.: 45.00  
##  Median : 70.00   Median : 65.00   Median : 70.00   Median : 65.00  
##  Mean   : 74.66   Mean   : 73.02   Mean   : 72.29   Mean   : 68.81  
##  3rd Qu.: 90.00   3rd Qu.: 95.00   3rd Qu.: 90.00   3rd Qu.: 90.00  
##  Max.   :250.00   Max.   :194.00   Max.   :250.00   Max.   :200.00

Use of names() and dimnames() command to view the column and row elements of an Object.

rownames(d2)
##    [1] "1"    "2"    "3"    "4"    "5"    "6"    "7"    "8"    "9"    "10"  
##   [11] "11"   "12"   "13"   "14"   "15"   "16"   "17"   "18"   "19"   "20"  
##   [21] "21"   "22"   "23"   "24"   "25"   "26"   "27"   "28"   "29"   "30"  
##   [31] "31"   "32"   "33"   "34"   "35"   "36"   "37"   "38"   "39"   "40"  
##   [41] "41"   "42"   "43"   "44"   "45"   "46"   "47"   "48"   "49"   "50"  
##   [51] "51"   "52"   "53"   "54"   "55"   "56"   "57"   "58"   "59"   "60"  
##   [61] "61"   "62"   "63"   "64"   "65"   "66"   "67"   "68"   "69"   "70"  
##   [71] "71"   "72"   "73"   "74"   "75"   "76"   "77"   "78"   "79"   "80"  
##   [81] "81"   "82"   "83"   "84"   "85"   "86"   "87"   "88"   "89"   "90"  
##   [91] "91"   "92"   "93"   "94"   "95"   "96"   "97"   "98"   "99"   "100" 
##  [101] "101"  "102"  "103"  "104"  "105"  "106"  "107"  "108"  "109"  "110" 
##  [111] "111"  "112"  "113"  "114"  "115"  "116"  "117"  "118"  "119"  "120" 
##  [121] "121"  "122"  "123"  "124"  "125"  "126"  "127"  "128"  "129"  "130" 
##  [131] "131"  "132"  "133"  "134"  "135"  "136"  "137"  "138"  "139"  "140" 
##  [141] "141"  "142"  "143"  "144"  "145"  "146"  "147"  "148"  "149"  "150" 
##  [151] "151"  "152"  "153"  "154"  "155"  "156"  "157"  "158"  "159"  "160" 
##  [161] "161"  "162"  "163"  "164"  "165"  "166"  "167"  "168"  "169"  "170" 
##  [171] "171"  "172"  "173"  "174"  "175"  "176"  "177"  "178"  "179"  "180" 
##  [181] "181"  "182"  "183"  "184"  "185"  "186"  "187"  "188"  "189"  "190" 
##  [191] "191"  "192"  "193"  "194"  "195"  "196"  "197"  "198"  "199"  "200" 
##  [201] "201"  "202"  "203"  "204"  "205"  "206"  "207"  "208"  "209"  "210" 
##  [211] "211"  "212"  "213"  "214"  "215"  "216"  "217"  "218"  "219"  "220" 
##  [221] "221"  "222"  "223"  "224"  "225"  "226"  "227"  "228"  "229"  "230" 
##  [231] "231"  "232"  "233"  "234"  "235"  "236"  "237"  "238"  "239"  "240" 
##  [241] "241"  "242"  "243"  "244"  "245"  "246"  "247"  "248"  "249"  "250" 
##  [251] "251"  "252"  "253"  "254"  "255"  "256"  "257"  "258"  "259"  "260" 
##  [261] "261"  "262"  "263"  "264"  "265"  "266"  "267"  "268"  "269"  "270" 
##  [271] "271"  "272"  "273"  "274"  "275"  "276"  "277"  "278"  "279"  "280" 
##  [281] "281"  "282"  "283"  "284"  "285"  "286"  "287"  "288"  "289"  "290" 
##  [291] "291"  "292"  "293"  "294"  "295"  "296"  "297"  "298"  "299"  "300" 
##  [301] "301"  "302"  "303"  "304"  "305"  "306"  "307"  "308"  "309"  "310" 
##  [311] "311"  "312"  "313"  "314"  "315"  "316"  "317"  "318"  "319"  "320" 
##  [321] "321"  "322"  "323"  "324"  "325"  "326"  "327"  "328"  "329"  "330" 
##  [331] "331"  "332"  "333"  "334"  "335"  "336"  "337"  "338"  "339"  "340" 
##  [341] "341"  "342"  "343"  "344"  "345"  "346"  "347"  "348"  "349"  "350" 
##  [351] "351"  "352"  "353"  "354"  "355"  "356"  "357"  "358"  "359"  "360" 
##  [361] "361"  "362"  "363"  "364"  "365"  "366"  "367"  "368"  "369"  "370" 
##  [371] "371"  "372"  "373"  "374"  "375"  "376"  "377"  "378"  "379"  "380" 
##  [381] "381"  "382"  "383"  "384"  "385"  "386"  "387"  "388"  "389"  "390" 
##  [391] "391"  "392"  "393"  "394"  "395"  "396"  "397"  "398"  "399"  "400" 
##  [401] "401"  "402"  "403"  "404"  "405"  "406"  "407"  "408"  "409"  "410" 
##  [411] "411"  "412"  "413"  "414"  "415"  "416"  "417"  "418"  "419"  "420" 
##  [421] "421"  "422"  "423"  "424"  "425"  "426"  "427"  "428"  "429"  "430" 
##  [431] "431"  "432"  "433"  "434"  "435"  "436"  "437"  "438"  "439"  "440" 
##  [441] "441"  "442"  "443"  "444"  "445"  "446"  "447"  "448"  "449"  "450" 
##  [451] "451"  "452"  "453"  "454"  "455"  "456"  "457"  "458"  "459"  "460" 
##  [461] "461"  "462"  "463"  "464"  "465"  "466"  "467"  "468"  "469"  "470" 
##  [471] "471"  "472"  "473"  "474"  "475"  "476"  "477"  "478"  "479"  "480" 
##  [481] "481"  "482"  "483"  "484"  "485"  "486"  "487"  "488"  "489"  "490" 
##  [491] "491"  "492"  "493"  "494"  "495"  "496"  "497"  "498"  "499"  "500" 
##  [501] "501"  "502"  "503"  "504"  "505"  "506"  "507"  "508"  "509"  "510" 
##  [511] "511"  "512"  "513"  "514"  "515"  "516"  "517"  "518"  "519"  "520" 
##  [521] "521"  "522"  "523"  "524"  "525"  "526"  "527"  "528"  "529"  "530" 
##  [531] "531"  "532"  "533"  "534"  "535"  "536"  "537"  "538"  "539"  "540" 
##  [541] "541"  "542"  "543"  "544"  "545"  "546"  "547"  "548"  "549"  "550" 
##  [551] "551"  "552"  "553"  "554"  "555"  "556"  "557"  "558"  "559"  "560" 
##  [561] "561"  "562"  "563"  "564"  "565"  "566"  "567"  "568"  "569"  "570" 
##  [571] "571"  "572"  "573"  "574"  "575"  "576"  "577"  "578"  "579"  "580" 
##  [581] "581"  "582"  "583"  "584"  "585"  "586"  "587"  "588"  "589"  "590" 
##  [591] "591"  "592"  "593"  "594"  "595"  "596"  "597"  "598"  "599"  "600" 
##  [601] "601"  "602"  "603"  "604"  "605"  "606"  "607"  "608"  "609"  "610" 
##  [611] "611"  "612"  "613"  "614"  "615"  "616"  "617"  "618"  "619"  "620" 
##  [621] "621"  "622"  "623"  "624"  "625"  "626"  "627"  "628"  "629"  "630" 
##  [631] "631"  "632"  "633"  "634"  "635"  "636"  "637"  "638"  "639"  "640" 
##  [641] "641"  "642"  "643"  "644"  "645"  "646"  "647"  "648"  "649"  "650" 
##  [651] "651"  "652"  "653"  "654"  "655"  "656"  "657"  "658"  "659"  "660" 
##  [661] "661"  "662"  "663"  "664"  "665"  "666"  "667"  "668"  "669"  "670" 
##  [671] "671"  "672"  "673"  "674"  "675"  "676"  "677"  "678"  "679"  "680" 
##  [681] "681"  "682"  "683"  "684"  "685"  "686"  "687"  "688"  "689"  "690" 
##  [691] "691"  "692"  "693"  "694"  "695"  "696"  "697"  "698"  "699"  "700" 
##  [701] "701"  "702"  "703"  "704"  "705"  "706"  "707"  "708"  "709"  "710" 
##  [711] "711"  "712"  "713"  "714"  "715"  "716"  "717"  "718"  "719"  "720" 
##  [721] "721"  "722"  "723"  "724"  "725"  "726"  "727"  "728"  "729"  "730" 
##  [731] "731"  "732"  "733"  "734"  "735"  "736"  "737"  "738"  "739"  "740" 
##  [741] "741"  "742"  "743"  "744"  "745"  "746"  "747"  "748"  "749"  "750" 
##  [751] "751"  "752"  "753"  "754"  "755"  "756"  "757"  "758"  "759"  "760" 
##  [761] "761"  "762"  "763"  "764"  "765"  "766"  "767"  "768"  "769"  "770" 
##  [771] "771"  "772"  "773"  "774"  "775"  "776"  "777"  "778"  "779"  "780" 
##  [781] "781"  "782"  "783"  "784"  "785"  "786"  "787"  "788"  "789"  "790" 
##  [791] "791"  "792"  "793"  "794"  "795"  "796"  "797"  "798"  "799"  "800" 
##  [801] "801"  "802"  "803"  "804"  "805"  "806"  "807"  "808"  "809"  "810" 
##  [811] "811"  "812"  "813"  "814"  "815"  "816"  "817"  "818"  "819"  "820" 
##  [821] "821"  "822"  "823"  "824"  "825"  "826"  "827"  "828"  "829"  "830" 
##  [831] "831"  "832"  "833"  "834"  "835"  "836"  "837"  "838"  "839"  "840" 
##  [841] "841"  "842"  "843"  "844"  "845"  "846"  "847"  "848"  "849"  "850" 
##  [851] "851"  "852"  "853"  "854"  "855"  "856"  "857"  "858"  "859"  "860" 
##  [861] "861"  "862"  "863"  "864"  "865"  "866"  "867"  "868"  "869"  "870" 
##  [871] "871"  "872"  "873"  "874"  "875"  "876"  "877"  "878"  "879"  "880" 
##  [881] "881"  "882"  "883"  "884"  "885"  "886"  "887"  "888"  "889"  "890" 
##  [891] "891"  "892"  "893"  "894"  "895"  "896"  "897"  "898"  "899"  "900" 
##  [901] "901"  "902"  "903"  "904"  "905"  "906"  "907"  "908"  "909"  "910" 
##  [911] "911"  "912"  "913"  "914"  "915"  "916"  "917"  "918"  "919"  "920" 
##  [921] "921"  "922"  "923"  "924"  "925"  "926"  "927"  "928"  "929"  "930" 
##  [931] "931"  "932"  "933"  "934"  "935"  "936"  "937"  "938"  "939"  "940" 
##  [941] "941"  "942"  "943"  "944"  "945"  "946"  "947"  "948"  "949"  "950" 
##  [951] "951"  "952"  "953"  "954"  "955"  "956"  "957"  "958"  "959"  "960" 
##  [961] "961"  "962"  "963"  "964"  "965"  "966"  "967"  "968"  "969"  "970" 
##  [971] "971"  "972"  "973"  "974"  "975"  "976"  "977"  "978"  "979"  "980" 
##  [981] "981"  "982"  "983"  "984"  "985"  "986"  "987"  "988"  "989"  "990" 
##  [991] "991"  "992"  "993"  "994"  "995"  "996"  "997"  "998"  "999"  "1000"
## [1001] "1001" "1002" "1003" "1004" "1005" "1006" "1007" "1008" "1009" "1010"
## [1011] "1011" "1012" "1013" "1014" "1015" "1016" "1017" "1018" "1019" "1020"
## [1021] "1021" "1022" "1023" "1024" "1025" "1026" "1027" "1028" "1029" "1030"
## [1031] "1031" "1032" "1033" "1034" "1035" "1036" "1037" "1038" "1039" "1040"
## [1041] "1041" "1042" "1043" "1044" "1045"
colnames(d2)
## [1] "Name"       "Total"      "HP"         "Attack"     "Defence"   
## [6] "Sp_attack"  "Sp_defence" "Speed"
plot(d2)

Summarizing samples:

list(d2)
## [[1]]
##                   Name Total  HP Attack Defence Sp_attack Sp_defence Speed
## 1            Bulbasaur   318  45     49      49        65         65    45
## 2              Ivysaur   405  60     62      63        80         80    60
## 3             Venusaur   525  80     82      83       100        100    80
## 4        Mega Venusaur   625  80    100     123       122        120    80
## 5           Charmander   309  39     52      43        60         50    65
## 6           Charmeleon   405  58     64      58        80         65    80
## 7            Charizard   534  78     84      78       109         85   100
## 8       Mega Charizard   634  78    130     111       130         85   100
## 9     Mega Charizard X   634  78    104      78       159        115   100
## 10            Squirtle   314  44     48      65        50         64    43
## 11           Wartortle   405  59     63      80        65         80    58
## 12           Blastoise   530  79     83     100        85        105    78
## 13      Mega Blastoise   630  79    103     120       135        115    78
## 14            Caterpie   195  45     30      35        20         20    45
## 15             Metapod   205  50     20      55        25         25    30
## 16          Butterfree   395  60     45      50        90         80    70
## 17              Weedle   195  40     35      30        20         20    50
## 18              Kakuna   205  45     25      50        25         25    35
## 19            Beedrill   395  65     90      40        45         80    75
## 20       Mega Beedrill   495  65    150      40        15         80   145
## 21              Pidgey   251  40     45      40        35         35    56
## 22           Pidgeotto   349  63     60      55        50         50    71
## 23             Pidgeot   479  83     80      75        70         70   101
## 24        Mega Pidgeot   579  83     80      80       135         80   121
## 25             Rattata   253  30     56      35        25         35    72
## 26        Mega Rattata   253  30     56      35        25         35    72
## 27            Raticate   413  55     81      60        50         70    97
## 28       Mega Raticate   413  75     71      70        40         80    77
## 29             Spearow   262  40     60      30        31         31    70
## 30              Fearow   442  65     90      65        61         61   100
## 31               Ekans   288  35     60      44        40         54    55
## 32               Arbok   448  60     95      69        65         79    80
## 33             Pikachu   320  35     55      40        50         50    90
## 34        Mega Pikachu   430  45     80      50        75         60   120
## 35              Raichu   485  60     90      55        90         80   110
## 36         Mega Raichu   485  60     85      50        95         85   110
## 37           Sandshrew   300  50     75      85        20         30    40
## 38      Mega Sandshrew   300  50     75      90        10         35    40
## 39           Sandslash   450  75    100     110        45         55    65
## 40      Mega Sandslash   450  75    100     120        25         65    65
## 41          Nidoranâ\231\200   275  55     47      52        40         40    41
## 42            Nidorina   365  70     62      67        55         55    56
## 43           Nidoqueen   505  90     92      87        75         85    76
## 44          Nidoranâ\231‚   273  46     57      40        40         40    50
## 45            Nidorino   365  61     72      57        55         55    65
## 46            Nidoking   505  81    102      77        85         75    85
## 47            Clefairy   323  70     45      48        60         65    35
## 48            Clefable   483  95     70      73        95         90    60
## 49              Vulpix   299  38     41      40        50         65    65
## 50         Mega Vulpix   299  38     41      40        50         65    65
## 51           Ninetales   505  73     76      75        81        100   100
## 52      Mega Ninetales   505  73     67      75        81        100   109
## 53          Jigglypuff   270 115     45      20        45         25    20
## 54          Wigglytuff   435 140     70      45        85         50    45
## 55               Zubat   245  40     45      35        30         40    55
## 56              Golbat   455  75     80      70        65         75    90
## 57              Oddish   320  45     50      55        75         65    30
## 58               Gloom   395  60     65      70        85         75    40
## 59           Vileplume   490  75     80      85       110         90    50
## 60               Paras   285  35     70      55        45         55    25
## 61            Parasect   405  60     95      80        60         80    30
## 62             Venonat   305  60     55      50        40         55    45
## 63            Venomoth   450  70     65      60        90         75    90
## 64             Diglett   265  10     55      25        35         45    95
## 65        Mega Diglett   265  10     55      30        35         45    90
## 66             Dugtrio   425  35    100      50        50         70   120
## 67        Mega Dugtrio   425  35    100      60        50         70   110
## 68              Meowth   290  40     45      35        40         40    90
## 69         Mega Meowth   290  40     35      35        50         40    90
## 70       Mega Meowth X   290  50     65      55        40         40    40
## 71             Persian   440  65     70      60        65         65   115
## 72        Mega Persian   440  65     60      60        75         65   115
## 73             Psyduck   320  50     52      48        65         50    55
## 74             Golduck   500  80     82      78        95         80    85
## 75              Mankey   305  40     80      35        35         45    70
## 76            Primeape   455  65    105      60        60         70    95
## 77           Growlithe   350  55     70      45        70         50    60
## 78            Arcanine   555  90    110      80       100         80    95
## 79             Poliwag   300  40     50      40        40         40    90
## 80           Poliwhirl   385  65     65      65        50         50    90
## 81           Poliwrath   510  90     95      95        70         90    70
## 82                Abra   310  25     20      15       105         55    90
## 83             Kadabra   400  40     35      30       120         70   105
## 84            Alakazam   500  55     50      45       135         95   120
## 85       Mega Alakazam   600  55     50      65       175        105   150
## 86              Machop   305  70     80      50        35         35    35
## 87             Machoke   405  80    100      70        50         60    45
## 88             Machamp   505  90    130      80        65         85    55
## 89          Bellsprout   300  50     75      35        70         30    40
## 90          Weepinbell   390  65     90      50        85         45    55
## 91          Victreebel   490  80    105      65       100         70    70
## 92           Tentacool   335  40     40      35        50        100    70
## 93          Tentacruel   515  80     70      65        80        120   100
## 94             Geodude   300  40     80     100        30         30    20
## 95        Mega Geodude   300  40     80     100        30         30    20
## 96            Graveler   390  55     95     115        45         45    35
## 97       Mega Graveler   390  55     95     115        45         45    35
## 98               Golem   495  80    120     130        55         65    45
## 99          Mega Golem   495  80    120     130        55         65    45
## 100             Ponyta   410  50     85      55        65         65    90
## 101        Mega Ponyta   410  50     85      55        65         65    90
## 102           Rapidash   500  65    100      70        80         80   105
## 103      Mega Rapidash   500  65    100      70        80         80   105
## 104           Slowpoke   315  90     65      65        40         40    15
## 105      Mega Slowpoke   315  90     65      65        40         40    15
## 106            Slowbro   490  95     75     110       100         80    30
## 107       Mega Slowbro   590  95     75     180       130         80    30
## 108     Mega Slowbro X   490  95    100      95       100         70    30
## 109          Magnemite   325  25     35      70        95         55    45
## 110           Magneton   465  50     60      95       120         70    70
## 111         Farfetch'd   377  52     90      55        58         62    60
## 112    Mega Farfetch'd   377  52     95      55        58         62    55
## 113              Doduo   310  35     85      45        35         35    75
## 114             Dodrio   470  60    110      70        60         60   110
## 115               Seel   325  65     45      55        45         70    45
## 116            Dewgong   475  90     70      80        70         95    70
## 117             Grimer   325  80     80      50        40         50    25
## 118        Mega Grimer   325  80     80      50        40         50    25
## 119                Muk   500 105    105      75        65        100    50
## 120           Mega Muk   500 105    105      75        65        100    50
## 121           Shellder   305  30     65     100        45         25    40
## 122           Cloyster   525  50     95     180        85         45    70
## 123             Gastly   310  30     35      30       100         35    80
## 124            Haunter   405  45     50      45       115         55    95
## 125             Gengar   500  60     65      60       130         75   110
## 126        Mega Gengar   600  60     65      80       170         95   130
## 127               Onix   385  35     45     160        30         45    70
## 128            Drowzee   328  60     48      45        43         90    42
## 129              Hypno   483  85     73      70        73        115    67
## 130             Krabby   325  30    105      90        25         25    50
## 131            Kingler   475  55    130     115        50         50    75
## 132            Voltorb   330  40     30      50        55         55   100
## 133          Electrode   490  60     50      70        80         80   150
## 134          Exeggcute   325  60     40      80        60         45    40
## 135          Exeggutor   530  95     95      85       125         75    55
## 136     Mega Exeggutor   530  95    105      85       125         75    45
## 137             Cubone   320  50     50      95        40         50    35
## 138            Marowak   425  60     80     110        50         80    45
## 139       Mega Marowak   425  60     80     110        50         80    45
## 140          Hitmonlee   455  50    120      53        35        110    87
## 141         Hitmonchan   455  50    105      79        35        110    76
## 142          Lickitung   385  90     55      75        60         75    30
## 143            Koffing   340  40     65      95        60         45    35
## 144            Weezing   490  65     90     120        85         70    60
## 145       Mega Weezing   490  65     90     120        85         70    60
## 146            Rhyhorn   345  80     85      95        30         30    25
## 147             Rhydon   485 105    130     120        45         45    40
## 148            Chansey   450 250      5       5        35        105    50
## 149            Tangela   435  65     55     115       100         40    60
## 150         Kangaskhan   490 105     95      80        40         80    90
## 151    Mega Kangaskhan   590 105    125     100        60        100   100
## 152             Horsea   295  30     40      70        70         25    60
## 153             Seadra   440  55     65      95        95         45    85
## 154            Goldeen   320  45     67      60        35         50    63
## 155            Seaking   450  80     92      65        65         80    68
## 156             Staryu   340  30     45      55        70         55    85
## 157            Starmie   520  60     75      85       100         85   115
## 158           Mr. Mime   460  40     45      65       100        120    90
## 159      Mega Mr. Mime   460  50     65      65        90         90   100
## 160            Scyther   500  70    110      80        55         80   105
## 161               Jynx   455  65     50      35       115         95    95
## 162         Electabuzz   490  65     83      57        95         85   105
## 163             Magmar   495  65     95      57       100         85    93
## 164             Pinsir   500  65    125     100        55         70    85
## 165        Mega Pinsir   600  65    155     120        65         90   105
## 166             Tauros   490  75    100      95        40         70   110
## 167           Magikarp   200  20     10      55        15         20    80
## 168           Gyarados   540  95    125      79        60        100    81
## 169      Mega Gyarados   640  95    155     109        70        130    81
## 170             Lapras   535 130     85      80        85         95    60
## 171              Ditto   288  48     48      48        48         48    48
## 172              Eevee   325  55     55      50        45         65    55
## 173         Mega Eevee   435  65     75      70        65         85    75
## 174           Vaporeon   525 130     65      60       110         95    65
## 175            Jolteon   525  65     65      60       110         95   130
## 176            Flareon   525  65    130      60        95        110    65
## 177            Porygon   395  65     60      70        85         75    40
## 178            Omanyte   355  35     40     100        90         55    35
## 179            Omastar   495  70     60     125       115         70    55
## 180             Kabuto   355  30     80      90        55         45    55
## 181           Kabutops   495  60    115     105        65         70    80
## 182         Aerodactyl   515  80    105      65        60         75   130
## 183    Mega Aerodactyl   615  80    135      85        70         95   150
## 184            Snorlax   540 160    110      65        65        110    30
## 185           Articuno   580  90     85     100        95        125    85
## 186      Mega Articuno   580  90     85      85       125        100    95
## 187             Zapdos   580  90     90      85       125         90   100
## 188        Mega Zapdos   580  90    125      90        85         90   100
## 189            Moltres   580  90    100      90       125         85    90
## 190       Mega Moltres   580  90     85      90       100        125    90
## 191            Dratini   300  41     64      45        50         50    50
## 192          Dragonair   420  61     84      65        70         70    70
## 193          Dragonite   600  91    134      95       100        100    80
## 194             Mewtwo   680 106    110      90       154         90   130
## 195        Mega Mewtwo   780 106    190     100       154        100   130
## 196      Mega Mewtwo X   780 106    150      70       194        120   140
## 197                Mew   600 100    100     100       100        100   100
## 198          Chikorita   318  45     49      65        49         65    45
## 199            Bayleef   405  60     62      80        63         80    60
## 200           Meganium   525  80     82     100        83        100    80
## 201          Cyndaquil   309  39     52      43        60         50    65
## 202            Quilava   405  58     64      58        80         65    80
## 203         Typhlosion   534  78     84      78       109         85   100
## 204           Totodile   314  50     65      64        44         48    43
## 205           Croconaw   405  65     80      80        59         63    58
## 206         Feraligatr   530  85    105     100        79         83    78
## 207            Sentret   215  35     46      34        35         45    20
## 208             Furret   415  85     76      64        45         55    90
## 209           Hoothoot   262  60     30      30        36         56    50
## 210            Noctowl   452 100     50      50        86         96    70
## 211             Ledyba   265  40     20      30        40         80    55
## 212             Ledian   390  55     35      50        55        110    85
## 213           Spinarak   250  40     60      40        40         40    30
## 214            Ariados   400  70     90      70        60         70    40
## 215             Crobat   535  85     90      80        70         80   130
## 216           Chinchou   330  75     38      38        56         56    67
## 217            Lanturn   460 125     58      58        76         76    67
## 218              Pichu   205  20     40      15        35         35    60
## 219             Cleffa   218  50     25      28        45         55    15
## 220          Igglybuff   210  90     30      15        40         20    15
## 221             Togepi   245  35     20      65        40         65    20
## 222            Togetic   405  55     40      85        80        105    40
## 223               Natu   320  40     50      45        70         45    70
## 224               Xatu   470  65     75      70        95         70    95
## 225             Mareep   280  55     40      40        65         45    35
## 226            Flaaffy   365  70     55      55        80         60    45
## 227           Ampharos   510  90     75      85       115         90    55
## 228      Mega Ampharos   610  90     95     105       165        110    45
## 229          Bellossom   490  75     80      95        90        100    50
## 230             Marill   250  70     20      50        20         50    40
## 231          Azumarill   420 100     50      80        60         80    50
## 232          Sudowoodo   410  70    100     115        30         65    30
## 233           Politoed   500  90     75      75        90        100    70
## 234             Hoppip   250  35     35      40        35         55    50
## 235           Skiploom   340  55     45      50        45         65    80
## 236           Jumpluff   460  75     55      70        55         95   110
## 237              Aipom   360  55     70      55        40         55    85
## 238            Sunkern   180  30     30      30        30         30    30
## 239           Sunflora   425  75     75      55       105         85    30
## 240              Yanma   390  65     65      45        75         45    95
## 241             Wooper   210  55     45      45        25         25    15
## 242           Quagsire   430  95     85      85        65         65    35
## 243             Espeon   525  65     65      60       130         95   110
## 244            Umbreon   525  95     65     110        60        130    65
## 245            Murkrow   405  60     85      42        85         42    91
## 246           Slowking   490  95     75      80       100        110    30
## 247      Mega Slowking   490  95     65      80       110        110    30
## 248         Misdreavus   435  60     60      60        85         85    85
## 249              Unown   336  48     72      48        72         48    48
## 250          Wobbuffet   405 190     33      58        33         58    33
## 251          Girafarig   455  70     80      65        90         65    85
## 252             Pineco   290  50     65      90        35         35    15
## 253         Forretress   465  75     90     140        60         60    40
## 254          Dunsparce   415 100     70      70        65         65    45
## 255             Gligar   430  65     75     105        35         65    85
## 256            Steelix   510  75     85     200        55         65    30
## 257       Mega Steelix   610  75    125     230        55         95    30
## 258           Snubbull   300  60     80      50        40         40    30
## 259           Granbull   450  90    120      75        60         60    45
## 260           Qwilfish   440  65     95      85        55         55    85
## 261             Scizor   500  70    130     100        55         80    65
## 262        Mega Scizor   600  70    150     140        65        100    75
## 263            Shuckle   505  20     10     230        10        230     5
## 264          Heracross   500  80    125      75        40         95    85
## 265     Mega Heracross   600  80    185     115        40        105    75
## 266            Sneasel   430  55     95      55        35         75   115
## 267          Teddiursa   330  60     80      50        50         50    40
## 268           Ursaring   500  90    130      75        75         75    55
## 269             Slugma   250  40     40      40        70         40    20
## 270           Magcargo   430  60     50     120        90         80    30
## 271             Swinub   250  50     50      40        30         30    50
## 272          Piloswine   450 100    100      80        60         60    50
## 273            Corsola   410  65     55      95        65         95    35
## 274       Mega Corsola   410  60     55     100        65        100    30
## 275           Remoraid   300  35     65      35        65         35    65
## 276          Octillery   480  75    105      75       105         75    45
## 277           Delibird   330  45     55      45        65         45    75
## 278            Mantine   485  85     40      70        80        140    70
## 279           Skarmory   465  65     80     140        40         70    70
## 280           Houndour   330  45     60      30        80         50    65
## 281           Houndoom   500  75     90      50       110         80    95
## 282      Mega Houndoom   600  75     90      90       140         90   115
## 283            Kingdra   540  75     95      95        95         95    85
## 284             Phanpy   330  90     60      60        40         40    40
## 285            Donphan   500  90    120     120        60         60    50
## 286           Porygon2   515  85     80      90       105         95    60
## 287           Stantler   465  73     95      62        85         65    85
## 288           Smeargle   250  55     20      35        20         45    75
## 289            Tyrogue   210  35     35      35        35         35    35
## 290          Hitmontop   455  50     95      95        35        110    70
## 291           Smoochum   305  45     30      15        85         65    65
## 292             Elekid   360  45     63      37        65         55    95
## 293              Magby   365  45     75      37        70         55    83
## 294            Miltank   490  95     80     105        40         70   100
## 295            Blissey   540 255     10      10        75        135    55
## 296             Raikou   580  90     85      75       115        100   115
## 297              Entei   580 115    115      85        90         75   100
## 298            Suicune   580 100     75     115        90        115    85
## 299           Larvitar   300  50     64      50        45         50    41
## 300            Pupitar   410  70     84      70        65         70    51
## 301          Tyranitar   600 100    134     110        95        100    61
## 302     Mega Tyranitar   700 100    164     150        95        120    71
## 303              Lugia   680 106     90     130        90        154   110
## 304              Ho-oh   680 106    130      90       110        154    90
## 305             Celebi   600 100    100     100       100        100   100
## 306            Treecko   310  40     45      35        65         55    70
## 307            Grovyle   405  50     65      45        85         65    95
## 308           Sceptile   530  70     85      65       105         85   120
## 309      Mega Sceptile   630  70    110      75       145         85   145
## 310            Torchic   310  45     60      40        70         50    45
## 311          Combusken   405  60     85      60        85         60    55
## 312           Blaziken   530  80    120      70       110         70    80
## 313      Mega Blaziken   630  80    160      80       130         80   100
## 314             Mudkip   310  50     70      50        50         50    40
## 315          Marshtomp   405  70     85      70        60         70    50
## 316           Swampert   535 100    110      90        85         90    60
## 317      Mega Swampert   635 100    150     110        95        110    70
## 318          Poochyena   220  35     55      35        30         30    35
## 319          Mightyena   420  70     90      70        60         60    70
## 320          Zigzagoon   240  38     30      41        30         41    60
## 321     Mega Zigzagoon   240  38     30      41        30         41    60
## 322            Linoone   420  78     70      61        50         61   100
## 323       Mega Linoone   420  78     70      61        50         61   100
## 324            Wurmple   195  45     45      35        20         30    20
## 325            Silcoon   205  50     35      55        25         25    15
## 326          Beautifly   395  60     70      50       100         50    65
## 327            Cascoon   205  50     35      55        25         25    15
## 328             Dustox   385  60     50      70        50         90    65
## 329              Lotad   220  40     30      30        40         50    30
## 330             Lombre   340  60     50      50        60         70    50
## 331           Ludicolo   480  80     70      70        90        100    70
## 332             Seedot   220  40     40      50        30         30    30
## 333            Nuzleaf   340  70     70      40        60         40    60
## 334            Shiftry   480  90    100      60        90         60    80
## 335            Taillow   270  40     55      30        30         30    85
## 336            Swellow   455  60     85      60        75         50   125
## 337            Wingull   270  40     30      30        55         30    85
## 338           Pelipper   440  60     50     100        95         70    65
## 339              Ralts   198  28     25      25        45         35    40
## 340             Kirlia   278  38     35      35        65         55    50
## 341          Gardevoir   518  68     65      65       125        115    80
## 342     Mega Gardevoir   618  68     85      65       165        135   100
## 343            Surskit   269  40     30      32        50         52    65
## 344         Masquerain   454  70     60      62       100         82    80
## 345          Shroomish   295  60     40      60        40         60    35
## 346            Breloom   460  60    130      80        60         60    70
## 347            Slakoth   280  60     60      60        35         35    30
## 348           Vigoroth   440  80     80      80        55         55    90
## 349            Slaking   670 150    160     100        95         65   100
## 350            Nincada   266  31     45      90        30         30    40
## 351            Ninjask   456  61     90      45        50         50   160
## 352           Shedinja   236   1     90      45        30         30    40
## 353            Whismur   240  64     51      23        51         23    28
## 354            Loudred   360  84     71      43        71         43    48
## 355            Exploud   490 104     91      63        91         73    68
## 356           Makuhita   237  72     60      30        20         30    25
## 357           Hariyama   474 144    120      60        40         60    50
## 358            Azurill   190  50     20      40        20         40    20
## 359           Nosepass   375  30     45     135        45         90    30
## 360             Skitty   260  50     45      45        35         35    50
## 361           Delcatty   400  70     65      65        55         55    90
## 362            Sableye   380  50     75      75        65         65    50
## 363       Mega Sableye   480  50     85     125        85        115    20
## 364             Mawile   380  50     85      85        55         55    50
## 365        Mega Mawile   480  50    105     125        55         95    50
## 366               Aron   330  50     70     100        40         40    30
## 367             Lairon   430  60     90     140        50         50    40
## 368             Aggron   530  70    110     180        60         60    50
## 369        Mega Aggron   630  70    140     230        60         80    50
## 370           Meditite   280  30     40      55        40         55    60
## 371           Medicham   410  60     60      75        60         75    80
## 372      Mega Medicham   510  60    100      85        80         85   100
## 373          Electrike   295  40     45      40        65         40    65
## 374          Manectric   475  70     75      60       105         60   105
## 375     Mega Manectric   575  70     75      80       135         80   135
## 376             Plusle   405  60     50      40        85         75    95
## 377              Minun   405  60     40      50        75         85    95
## 378            Volbeat   430  65     73      75        47         85    85
## 379           Illumise   430  65     47      75        73         85    85
## 380            Roselia   400  50     60      45       100         80    65
## 381             Gulpin   302  70     43      53        43         53    40
## 382             Swalot   467 100     73      83        73         83    55
## 383           Carvanha   305  45     90      20        65         20    65
## 384           Sharpedo   460  70    120      40        95         40    95
## 385      Mega Sharpedo   560  70    140      70       110         65   105
## 386            Wailmer   400 130     70      35        70         35    60
## 387            Wailord   500 170     90      45        90         45    60
## 388              Numel   305  60     60      40        65         45    35
## 389           Camerupt   460  70    100      70       105         75    40
## 390      Mega Camerupt   560  70    120     100       145        105    20
## 391            Torkoal   470  70     85     140        85         70    20
## 392             Spoink   330  60     25      35        70         80    60
## 393            Grumpig   470  80     45      65        90        110    80
## 394             Spinda   360  60     60      60        60         60    60
## 395           Trapinch   290  45    100      45        45         45    10
## 396            Vibrava   340  50     70      50        50         50    70
## 397             Flygon   520  80    100      80        80         80   100
## 398             Cacnea   335  50     85      40        85         40    35
## 399           Cacturne   475  70    115      60       115         60    55
## 400             Swablu   310  45     40      60        40         75    50
## 401            Altaria   490  75     70      90        70        105    80
## 402       Mega Altaria   590  75    110     110       110        105    80
## 403           Zangoose   458  73    115      60        60         60    90
## 404            Seviper   458  73    100      60       100         60    65
## 405           Lunatone   460  90     55      65        95         85    70
## 406            Solrock   460  90     95      85        55         65    70
## 407           Barboach   288  50     48      43        46         41    60
## 408           Whiscash   468 110     78      73        76         71    60
## 409           Corphish   308  43     80      65        50         35    35
## 410          Crawdaunt   468  63    120      85        90         55    55
## 411             Baltoy   300  40     40      55        40         70    55
## 412            Claydol   500  60     70     105        70        120    75
## 413             Lileep   355  66     41      77        61         87    23
## 414            Cradily   495  86     81      97        81        107    43
## 415            Anorith   355  45     95      50        40         50    75
## 416            Armaldo   495  75    125     100        70         80    45
## 417             Feebas   200  20     15      20        10         55    80
## 418            Milotic   540  95     60      79       100        125    81
## 419           Castform   420  70     70      70        70         70    70
## 420      Mega Castform   420  70     70      70        70         70    70
## 421    Mega Castform X   420  70     70      70        70         70    70
## 422    Mega Castform X   420  70     70      70        70         70    70
## 423            Kecleon   440  60     90      70        60        120    40
## 424            Shuppet   295  44     75      35        63         33    45
## 425            Banette   455  64    115      65        83         63    65
## 426       Mega Banette   555  64    165      75        93         83    75
## 427            Duskull   295  20     40      90        30         90    25
## 428           Dusclops   455  40     70     130        60        130    25
## 429            Tropius   460  99     68      83        72         87    51
## 430           Chimecho   455  75     50      80        95         90    65
## 431              Absol   465  65    130      60        75         60    75
## 432         Mega Absol   565  65    150      60       115         60   115
## 433             Wynaut   260  95     23      48        23         48    23
## 434            Snorunt   300  50     50      50        50         50    50
## 435             Glalie   480  80     80      80        80         80    80
## 436        Mega Glalie   580  80    120      80       120         80   100
## 437             Spheal   290  70     40      50        55         50    25
## 438             Sealeo   410  90     60      70        75         70    45
## 439            Walrein   530 110     80      90        95         90    65
## 440           Clamperl   345  35     64      85        74         55    32
## 441            Huntail   485  55    104     105        94         75    52
## 442           Gorebyss   485  55     84     105       114         75    52
## 443          Relicanth   485 100     90     130        45         65    55
## 444            Luvdisc   330  43     30      55        40         65    97
## 445              Bagon   300  45     75      60        40         30    50
## 446            Shelgon   420  65     95     100        60         50    50
## 447          Salamence   600  95    135      80       110         80   100
## 448     Mega Salamence   700  95    145     130       120         90   120
## 449             Beldum   300  40     55      80        35         60    30
## 450             Metang   420  60     75     100        55         80    50
## 451          Metagross   600  80    135     130        95         90    70
## 452     Mega Metagross   700  80    145     150       105        110   110
## 453           Regirock   580  80    100     200        50        100    50
## 454             Regice   580  80     50     100       100        200    50
## 455          Registeel   580  80     75     150        75        150    50
## 456             Latias   600  80     80      90       110        130   110
## 457        Mega Latias   700  80    100     120       140        150   110
## 458             Latios   600  80     90      80       130        110   110
## 459        Mega Latios   700  80    130     100       160        120   110
## 460             Kyogre   670 100    100      90       150        140    90
## 461        Mega Kyogre   770 100    150      90       180        160    90
## 462            Groudon   670 100    150     140       100         90    90
## 463       Mega Groudon   770 100    180     160       150         90    90
## 464           Rayquaza   680 105    150      90       150         90    95
## 465      Mega Rayquaza   780 105    180     100       180        100   115
## 466            Jirachi   600 100    100     100       100        100   100
## 467             Deoxys   600  50    150      50       150         50   150
## 468        Mega Deoxys   600  50    180      20       180         20   150
## 469      Mega Deoxys X   600  50     70     160        70        160    90
## 470      Mega Deoxys X   600  50     95      90        95         90   180
## 471            Turtwig   318  55     68      64        45         55    31
## 472             Grotle   405  75     89      85        55         65    36
## 473           Torterra   525  95    109     105        75         85    56
## 474           Chimchar   309  44     58      44        58         44    61
## 475           Monferno   405  64     78      52        78         52    81
## 476          Infernape   534  76    104      71       104         71   108
## 477             Piplup   314  53     51      53        61         56    40
## 478           Prinplup   405  64     66      68        81         76    50
## 479           Empoleon   530  84     86      88       111        101    60
## 480             Starly   245  40     55      30        30         30    60
## 481           Staravia   340  55     75      50        40         40    80
## 482          Staraptor   485  85    120      70        50         60   100
## 483             Bidoof   250  59     45      40        35         40    31
## 484            Bibarel   410  79     85      60        55         60    71
## 485          Kricketot   194  37     25      41        25         41    25
## 486         Kricketune   384  77     85      51        55         51    65
## 487              Shinx   263  45     65      34        40         34    45
## 488              Luxio   363  60     85      49        60         49    60
## 489             Luxray   523  80    120      79        95         79    70
## 490              Budew   280  40     30      35        50         70    55
## 491           Roserade   515  60     70      65       125        105    90
## 492           Cranidos   350  67    125      40        30         30    58
## 493          Rampardos   495  97    165      60        65         50    58
## 494           Shieldon   350  30     42     118        42         88    30
## 495          Bastiodon   495  60     52     168        47        138    30
## 496              Burmy   224  40     29      45        29         45    36
## 497           Wormadam   424  60     59      85        79        105    36
## 498      Mega Wormadam   424  60     79     105        59         85    36
## 499    Mega Wormadam X   424  60     69      95        69         95    36
## 500             Mothim   424  70     94      50        94         50    66
## 501             Combee   244  30     30      42        30         42    70
## 502          Vespiquen   474  70     80     102        80        102    40
## 503          Pachirisu   405  60     45      70        45         90    95
## 504             Buizel   330  55     65      35        60         30    85
## 505           Floatzel   495  85    105      55        85         50   115
## 506            Cherubi   275  45     35      45        62         53    35
## 507            Cherrim   450  70     60      70        87         78    85
## 508            Shellos   325  76     48      48        57         62    34
## 509          Gastrodon   475 111     83      68        92         82    39
## 510            Ambipom   482  75    100      66        60         66   115
## 511           Drifloon   348  90     50      34        60         44    70
## 512           Drifblim   498 150     80      44        90         54    80
## 513            Buneary   350  55     66      44        44         56    85
## 514            Lopunny   480  65     76      84        54         96   105
## 515       Mega Lopunny   580  65    136      94        54         96   135
## 516          Mismagius   495  60     60      60       105        105   105
## 517          Honchkrow   505 100    125      52       105         52    71
## 518            Glameow   310  49     55      42        42         37    85
## 519            Purugly   452  71     82      64        64         59   112
## 520          Chingling   285  45     30      50        65         50    45
## 521             Stunky   329  63     63      47        41         41    74
## 522           Skuntank   479 103     93      67        71         61    84
## 523            Bronzor   300  57     24      86        24         86    23
## 524           Bronzong   500  67     89     116        79        116    33
## 525             Bonsly   290  50     80      95        10         45    10
## 526           Mime Jr.   310  20     25      45        70         90    60
## 527            Happiny   220 100      5       5        15         65    30
## 528             Chatot   411  76     65      45        92         42    91
## 529          Spiritomb   485  50     92     108        92        108    35
## 530              Gible   300  58     70      45        40         45    42
## 531             Gabite   410  68     90      65        50         55    82
## 532           Garchomp   600 108    130      95        80         85   102
## 533      Mega Garchomp   700 108    170     115       120         95    92
## 534           Munchlax   390 135     85      40        40         85     5
## 535              Riolu   285  40     70      40        35         40    60
## 536            Lucario   525  70    110      70       115         70    90
## 537       Mega Lucario   625  70    145      88       140         70   112
## 538         Hippopotas   330  68     72      78        38         42    32
## 539          Hippowdon   525 108    112     118        68         72    47
## 540            Skorupi   330  40     50      90        30         55    65
## 541            Drapion   500  70     90     110        60         75    95
## 542           Croagunk   300  48     61      40        61         40    50
## 543          Toxicroak   490  83    106      65        86         65    85
## 544          Carnivine   454  74    100      72        90         72    46
## 545            Finneon   330  49     49      56        49         61    66
## 546           Lumineon   460  69     69      76        69         86    91
## 547            Mantyke   345  45     20      50        60        120    50
## 548             Snover   334  60     62      50        62         60    40
## 549          Abomasnow   494  90     92      75        92         85    60
## 550     Mega Abomasnow   594  90    132     105       132        105    30
## 551            Weavile   510  70    120      65        45         85   125
## 552          Magnezone   535  70     70     115       130         90    60
## 553         Lickilicky   515 110     85      95        80         95    50
## 554          Rhyperior   535 115    140     130        55         55    40
## 555          Tangrowth   535 100    100     125       110         50    50
## 556         Electivire   540  75    123      67        95         85    95
## 557          Magmortar   540  75     95      67       125         95    83
## 558           Togekiss   545  85     50      95       120        115    80
## 559            Yanmega   515  86     76      86       116         56    95
## 560            Leafeon   525  65    110     130        60         65    95
## 561            Glaceon   525  65     60     110       130         95    65
## 562            Gliscor   510  75     95     125        45         75    95
## 563          Mamoswine   530 110    130      80        70         60    80
## 564          Porygon-Z   535  85     80      70       135         75    90
## 565            Gallade   518  68    125      65        65        115    80
## 566       Mega Gallade   618  68    165      95        65        115   110
## 567          Probopass   525  60     55     145        75        150    40
## 568           Dusknoir   525  45    100     135        65        135    45
## 569           Froslass   480  70     80      70        80         70   110
## 570              Rotom   440  50     50      77        95         77    91
## 571         Mega Rotom   520  50     65     107       105        107    86
## 572       Mega Rotom X   520  50     65     107       105        107    86
## 573       Mega Rotom X   520  50     65     107       105        107    86
## 574       Mega Rotom X   520  50     65     107       105        107    86
## 575       Mega Rotom X   520  50     65     107       105        107    86
## 576               Uxie   580  75     75     130        75        130    95
## 577            Mesprit   580  80    105     105       105        105    80
## 578              Azelf   580  75    125      70       125         70   115
## 579             Dialga   680 100    120     120       150        100    90
## 580             Palkia   680  90    120     100       150        120   100
## 581            Heatran   600  91     90     106       130        106    77
## 582          Regigigas   670 110    160     110        80        110   100
## 583           Giratina   680 150    100     120       100        120    90
## 584      Mega Giratina   680 150    120     100       120        100    90
## 585          Cresselia   600 120     70     120        75        130    85
## 586             Phione   480  80     80      80        80         80    80
## 587            Manaphy   600 100    100     100       100        100   100
## 588            Darkrai   600  70     90      90       135         90   125
## 589            Shaymin   600 100    100     100       100        100   100
## 590       Mega Shaymin   600 100    103      75       120         75   127
## 591             Arceus   720 120    120     120       120        120   120
## 592            Victini   600 100    100     100       100        100   100
## 593              Snivy   308  45     45      55        45         55    63
## 594            Servine   413  60     60      75        60         75    83
## 595          Serperior   528  75     75      95        75         95   113
## 596              Tepig   308  65     63      45        45         45    45
## 597            Pignite   418  90     93      55        70         55    55
## 598             Emboar   528 110    123      65       100         65    65
## 599           Oshawott   308  55     55      45        63         45    45
## 600             Dewott   413  75     75      60        83         60    60
## 601           Samurott   528  95    100      85       108         70    70
## 602             Patrat   255  45     55      39        35         39    42
## 603            Watchog   420  60     85      69        60         69    77
## 604           Lillipup   275  45     60      45        25         45    55
## 605            Herdier   370  65     80      65        35         65    60
## 606          Stoutland   500  85    110      90        45         90    80
## 607           Purrloin   281  41     50      37        50         37    66
## 608            Liepard   446  64     88      50        88         50   106
## 609            Pansage   316  50     53      48        53         48    64
## 610           Simisage   498  75     98      63        98         63   101
## 611            Pansear   316  50     53      48        53         48    64
## 612           Simisear   498  75     98      63        98         63   101
## 613            Panpour   316  50     53      48        53         48    64
## 614           Simipour   498  75     98      63        98         63   101
## 615              Munna   292  76     25      45        67         55    24
## 616           Musharna   487 116     55      85       107         95    29
## 617             Pidove   264  50     55      50        36         30    43
## 618          Tranquill   358  62     77      62        50         42    65
## 619           Unfezant   488  80    115      80        65         55    93
## 620            Blitzle   295  45     60      32        50         32    76
## 621          Zebstrika   497  75    100      63        80         63   116
## 622         Roggenrola   280  55     75      85        25         25    15
## 623            Boldore   390  70    105     105        50         40    20
## 624           Gigalith   515  85    135     130        60         80    25
## 625             Woobat   323  65     45      43        55         43    72
## 626            Swoobat   425  67     57      55        77         55   114
## 627            Drilbur   328  60     85      40        30         45    68
## 628          Excadrill   508 110    135      60        50         65    88
## 629             Audino   445 103     60      86        60         86    50
## 630        Mega Audino   545 103     60     126        80        126    50
## 631            Timburr   305  75     80      55        25         35    35
## 632            Gurdurr   405  85    105      85        40         50    40
## 633         Conkeldurr   505 105    140      95        55         65    45
## 634            Tympole   294  50     50      40        50         40    64
## 635          Palpitoad   384  75     65      55        65         55    69
## 636         Seismitoad   509 105     95      75        85         75    74
## 637              Throh   465 120    100      85        30         85    45
## 638               Sawk   465  75    125      75        30         75    85
## 639           Sewaddle   310  45     53      70        40         60    42
## 640           Swadloon   380  55     63      90        50         80    42
## 641           Leavanny   500  75    103      80        70         80    92
## 642           Venipede   260  30     45      59        30         39    57
## 643         Whirlipede   360  40     55      99        40         79    47
## 644          Scolipede   485  60    100      89        55         69   112
## 645           Cottonee   280  40     27      60        37         50    66
## 646         Whimsicott   480  60     67      85        77         75   116
## 647            Petilil   280  45     35      50        70         50    30
## 648          Lilligant   480  70     60      75       110         75    90
## 649           Basculin   460  70     92      65        80         55    98
## 650      Mega Basculin   460  70     92      65        80         55    98
## 651            Sandile   292  50     72      35        35         35    65
## 652           Krokorok   351  60     82      45        45         45    74
## 653         Krookodile   519  95    117      80        65         70    92
## 654           Darumaka   315  70     90      45        15         45    50
## 655      Mega Darumaka   315  70     90      45        15         45    50
## 656         Darmanitan   480 105    140      55        30         55    95
## 657    Mega Darmanitan   540 105     30     105       140        105    55
## 658  Mega Darmanitan X   480 105    140      55        30         55    95
## 659  Mega Darmanitan X   540 105    160      55        30         55   135
## 660           Maractus   461  75     86      67       106         67    60
## 661            Dwebble   325  50     65      85        35         35    55
## 662            Crustle   485  70    105     125        65         75    45
## 663            Scraggy   348  50     75      70        35         70    48
## 664            Scrafty   488  65     90     115        45        115    58
## 665           Sigilyph   490  72     58      80       103         80    97
## 666             Yamask   303  38     30      85        55         65    30
## 667        Mega Yamask   303  38     55      85        30         65    30
## 668         Cofagrigus   483  58     50     145        95        105    30
## 669           Tirtouga   355  54     78     103        53         45    22
## 670         Carracosta   495  74    108     133        83         65    32
## 671             Archen   401  55    112      45        74         45    70
## 672           Archeops   567  75    140      65       112         65   110
## 673           Trubbish   329  50     50      62        40         62    65
## 674           Garbodor   474  80     95      82        60         82    75
## 675              Zorua   330  40     65      40        80         40    65
## 676            Zoroark   510  60    105      60       120         60   105
## 677           Minccino   300  55     50      40        40         40    75
## 678           Cinccino   470  75     95      60        65         60   115
## 679            Gothita   290  45     30      50        55         65    45
## 680          Gothorita   390  60     45      70        75         85    55
## 681         Gothitelle   490  70     55      95        95        110    65
## 682            Solosis   290  45     30      40       105         50    20
## 683            Duosion   370  65     40      50       125         60    30
## 684          Reuniclus   490 110     65      75       125         85    30
## 685           Ducklett   305  62     44      50        44         50    55
## 686             Swanna   473  75     87      63        87         63    98
## 687          Vanillite   305  36     50      50        65         60    44
## 688          Vanillish   395  51     65      65        80         75    59
## 689          Vanilluxe   535  71     95      85       110         95    79
## 690           Deerling   335  60     60      50        40         50    75
## 691           Sawsbuck   475  80    100      70        60         70    95
## 692             Emolga   428  55     75      60        75         60   103
## 693         Karrablast   315  50     75      45        40         45    60
## 694         Escavalier   495  70    135     105        60        105    20
## 695            Foongus   294  69     55      45        55         55    15
## 696          Amoonguss   464 114     85      70        85         80    30
## 697           Frillish   335  55     40      50        65         85    40
## 698          Jellicent   480 100     60      70        85        105    60
## 699          Alomomola   470 165     75      80        40         45    65
## 700             Joltik   319  50     47      50        57         50    65
## 701         Galvantula   472  70     77      60        97         60   108
## 702          Ferroseed   305  44     50      91        24         86    10
## 703         Ferrothorn   489  74     94     131        54        116    20
## 704              Klink   300  40     55      70        45         60    30
## 705              Klang   440  60     80      95        70         85    50
## 706          Klinklang   520  60    100     115        70         85    90
## 707             Tynamo   275  35     55      40        45         40    60
## 708          Eelektrik   405  65     85      70        75         70    40
## 709         Eelektross   515  85    115      80       105         80    50
## 710             Elgyem   335  55     55      55        85         55    30
## 711           Beheeyem   485  75     75      75       125         95    40
## 712            Litwick   275  50     30      55        65         55    20
## 713            Lampent   370  60     40      60        95         60    55
## 714         Chandelure   520  60     55      90       145         90    80
## 715               Axew   320  46     87      60        30         40    57
## 716            Fraxure   410  66    117      70        40         50    67
## 717            Haxorus   540  76    147      90        60         70    97
## 718            Cubchoo   305  55     70      40        60         40    40
## 719            Beartic   505  95    130      80        70         80    50
## 720          Cryogonal   515  80     50      50        95        135   105
## 721            Shelmet   305  50     40      85        40         65    25
## 722           Accelgor   495  80     70      40       100         60   145
## 723           Stunfisk   471 109     66      84        81         99    32
## 724      Mega Stunfisk   471 109     81      99        66         84    32
## 725            Mienfoo   350  45     85      50        55         50    65
## 726           Mienshao   510  65    125      60        95         60   105
## 727          Druddigon   485  77    120      90        60         90    48
## 728             Golett   303  59     74      50        35         50    35
## 729             Golurk   483  89    124      80        55         80    55
## 730           Pawniard   340  45     85      70        40         40    60
## 731            Bisharp   490  65    125     100        60         70    70
## 732         Bouffalant   490  95    110      95        40         95    55
## 733            Rufflet   350  70     83      50        37         50    60
## 734           Braviary   510 100    123      75        57         75    80
## 735            Vullaby   370  70     55      75        45         65    60
## 736          Mandibuzz   510 110     65     105        55         95    80
## 737            Heatmor   484  85     97      66       105         66    65
## 738             Durant   484  58    109     112        48         48   109
## 739              Deino   300  52     65      50        45         50    38
## 740           Zweilous   420  72     85      70        65         70    58
## 741          Hydreigon   600  92    105      90       125         90    98
## 742           Larvesta   360  55     85      55        50         55    60
## 743          Volcarona   550  85     60      65       135        105   100
## 744           Cobalion   580  91     90     129        90         72   108
## 745          Terrakion   580  91    129      90        72         90   108
## 746           Virizion   580  91     90      72        90        129   108
## 747           Tornadus   580  79    115      70       125         80   111
## 748      Mega Tornadus   580  79    100      80       110         90   121
## 749          Thundurus   580  79    115      70       125         80   111
## 750     Mega Thundurus   580  79    105      70       145         80   101
## 751           Reshiram   680 100    120     100       150        120    90
## 752             Zekrom   680 100    150     120       120        100    90
## 753           Landorus   600  89    125      90       115         80   101
## 754      Mega Landorus   600  89    145      90       105         80    91
## 755             Kyurem   660 125    130      90       130         90    95
## 756        Mega Kyurem   700 125    170     100       120         90    95
## 757      Mega Kyurem X   700 125    120      90       170        100    95
## 758             Keldeo   580  91     72      90       129         90   108
## 759        Mega Keldeo   580  91     72      90       129         90   108
## 760           Meloetta   600 100     77      77       128        128    90
## 761      Mega Meloetta   600 100    128      90        77         77   128
## 762           Genesect   600  71    120      95       120         95    99
## 763            Chespin   313  56     61      65        48         45    38
## 764          Quilladin   405  61     78      95        56         58    57
## 765         Chesnaught   530  88    107     122        74         75    64
## 766           Fennekin   307  40     45      40        62         60    60
## 767            Braixen   409  59     59      58        90         70    73
## 768            Delphox   534  75     69      72       114        100   104
## 769            Froakie   314  41     56      40        62         44    71
## 770          Frogadier   405  54     63      52        83         56    97
## 771           Greninja   530  72     95      67       103         71   122
## 772      Mega Greninja   640  72    145      67       153         71   132
## 773           Bunnelby   237  38     36      38        32         36    57
## 774          Diggersby   423  85     56      77        50         77    78
## 775         Fletchling   278  45     50      43        40         38    62
## 776        Fletchinder   382  62     73      55        56         52    84
## 777         Talonflame   499  78     81      71        74         69   126
## 778         Scatterbug   200  38     35      40        27         25    35
## 779             Spewpa   213  45     22      60        27         30    29
## 780           Vivillon   411  80     52      50        90         50    89
## 781             Litleo   369  62     50      58        73         54    72
## 782             Pyroar   507  86     68      72       109         66   106
## 783          Flabébé   303  44     38      39        61         79    42
## 784            Floette   371  54     45      47        75         98    52
## 785            Florges   552  78     65      68       112        154    75
## 786             Skiddo   350  66     65      48        62         57    52
## 787             Gogoat   531 123    100      62        97         81    68
## 788            Pancham   348  67     82      62        46         48    43
## 789            Pangoro   495  95    124      78        69         71    58
## 790            Furfrou   472  75     80      60        65         90   102
## 791             Espurr   355  62     48      54        63         60    68
## 792           Meowstic   466  74     48      76        83         81   104
## 793      Mega Meowstic   466  74     48      76        83         81   104
## 794            Honedge   325  45     80     100        35         37    28
## 795           Doublade   448  59    110     150        45         49    35
## 796          Aegislash   500  60     50     140        50        140    60
## 797     Mega Aegislash   500  60    140      50       140         50    60
## 798           Spritzee   341  78     52      60        63         65    23
## 799         Aromatisse   462 101     72      72        99         89    29
## 800            Swirlix   341  62     48      66        59         57    49
## 801           Slurpuff   480  82     80      86        85         75    72
## 802              Inkay   288  53     54      53        37         46    45
## 803            Malamar   482  86     92      88        68         75    73
## 804            Binacle   306  42     52      67        39         56    50
## 805         Barbaracle   500  72    105     115        54         86    68
## 806             Skrelp   320  50     60      60        60         60    30
## 807           Dragalge   494  65     75      90        97        123    44
## 808          Clauncher   330  50     53      62        58         63    44
## 809          Clawitzer   500  71     73      88       120         89    59
## 810         Helioptile   289  44     38      33        61         43    70
## 811          Heliolisk   481  62     55      52       109         94   109
## 812             Tyrunt   362  58     89      77        45         45    48
## 813          Tyrantrum   521  82    121     119        69         59    71
## 814             Amaura   362  77     59      50        67         63    46
## 815            Aurorus   521 123     77      72        99         92    58
## 816            Sylveon   525  95     65      65       110        130    60
## 817           Hawlucha   500  78     92      75        74         63   118
## 818            Dedenne   431  67     58      57        81         67   101
## 819            Carbink   500  50     50     150        50        150    50
## 820              Goomy   300  45     50      35        55         75    40
## 821            Sliggoo   452  68     75      53        83        113    60
## 822             Goodra   600  90    100      70       110        150    80
## 823             Klefki   470  57     80      91        80         87    75
## 824           Phantump   309  43     70      48        50         60    38
## 825          Trevenant   474  85    110      76        65         82    56
## 826          Pumpkaboo   335  49     66      70        44         55    51
## 827     Mega Pumpkaboo   335  44     66      70        44         55    56
## 828   Mega Pumpkaboo X   335  54     66      70        44         55    46
## 829   Mega Pumpkaboo X   335  59     66      70        44         55    41
## 830          Gourgeist   494  65     90     122        58         75    84
## 831     Mega Gourgeist   494  55     85     122        58         75    99
## 832   Mega Gourgeist X   494  75     95     122        58         75    69
## 833   Mega Gourgeist X   494  85    100     122        58         75    54
## 834           Bergmite   304  55     69      85        32         35    28
## 835            Avalugg   514  95    117     184        44         46    28
## 836             Noibat   245  40     30      35        45         40    55
## 837            Noivern   535  85     70      80        97         80   123
## 838            Xerneas   680 126    131      95       131         98    99
## 839            Yveltal   680 126    131      95       131         98    99
## 840            Zygarde   600 108    100     121        81         95    95
## 841       Mega Zygarde   486  54    100      71        61         85   115
## 842     Mega Zygarde X   708 216    100     121        91         95    85
## 843            Diancie   600  50    100     150       100        150    50
## 844       Mega Diancie   700  50    160     110       160        110   110
## 845              Hoopa   600  80    110      60       150        130    70
## 846         Mega Hoopa   680  80    160      60       170        130    80
## 847          Volcanion   600  80    110     120       130         90    70
## 848             Rowlet   320  68     55      55        50         50    42
## 849            Dartrix   420  78     75      75        70         70    52
## 850          Decidueye   530  78    107      75       100        100    70
## 851             Litten   320  45     65      40        60         40    70
## 852           Torracat   420  65     85      50        80         50    90
## 853         Incineroar   530  95    115      90        80         90    60
## 854            Popplio   320  50     54      54        66         56    40
## 855            Brionne   420  60     69      69        91         81    50
## 856          Primarina   530  80     74      74       126        116    60
## 857            Pikipek   265  35     75      30        30         30    65
## 858           Trumbeak   355  55     85      50        40         50    75
## 859          Toucannon   485  80    120      75        75         75    60
## 860            Yungoos   253  48     70      30        30         30    45
## 861           Gumshoos   418  88    110      60        55         60    45
## 862            Grubbin   300  47     62      45        55         45    46
## 863          Charjabug   400  57     82      95        55         75    36
## 864           Vikavolt   500  77     70      90       145         75    43
## 865         Crabrawler   338  47     82      57        42         47    63
## 866       Crabominable   478  97    132      77        62         67    43
## 867           Oricorio   476  75     70      70        98         70    93
## 868      Mega Oricorio   476  75     70      70        98         70    93
## 869    Mega Oricorio X   476  75     70      70        98         70    93
## 870    Mega Oricorio X   476  75     70      70        98         70    93
## 871           Cutiefly   304  40     45      40        55         40    84
## 872           Ribombee   464  60     55      60        95         70   124
## 873           Rockruff   280  45     65      40        30         40    60
## 874      Mega Rockruff   280  45     65      40        30         40    60
## 875           Lycanroc   487  75    115      65        55         65   112
## 876      Mega Lycanroc   487  85    115      75        55         75    82
## 877    Mega Lycanroc X   487  75    117      65        55         65   110
## 878         Wishiwashi   175  45     20      20        25         25    40
## 879    Mega Wishiwashi   620  45    140     130       140        135    30
## 880           Mareanie   305  50     53      62        43         52    45
## 881            Toxapex   495  50     63     152        53        142    35
## 882            Mudbray   385  70    100      70        45         55    45
## 883           Mudsdale   500 100    125     100        55         85    35
## 884           Dewpider   269  38     40      52        40         72    27
## 885          Araquanid   454  68     70      92        50        132    42
## 886           Fomantis   250  40     55      35        50         35    35
## 887           Lurantis   480  70    105      90        80         90    45
## 888           Morelull   285  40     35      55        65         75    15
## 889          Shiinotic   405  60     45      80        90        100    30
## 890           Salandit   320  48     44      40        71         40    77
## 891           Salazzle   480  68     64      60       111         60   117
## 892            Stufful   340  70     75      50        45         50    50
## 893             Bewear   500 120    125      80        55         60    60
## 894          Bounsweet   210  42     30      38        30         38    32
## 895            Steenee   290  52     40      48        40         48    62
## 896           Tsareena   510  72    120      98        50         98    72
## 897             Comfey   485  51     52      90        82        110   100
## 898           Oranguru   490  90     60      80        90        110    60
## 899          Passimian   490 100    120      90        40         60    80
## 900             Wimpod   230  25     35      40        20         30    80
## 901          Golisopod   530  75    125     140        60         90    40
## 902          Sandygast   320  55     55      80        70         45    15
## 903          Palossand   480  85     75     110       100         75    35
## 904          Pyukumuku   410  55     60     130        30        130     5
## 905         Type: Null   534  95     95      95        95         95    59
## 906           Silvally   570  95     95      95        95         95    95
## 907             Minior   440  60     60     100        60        100    60
## 908        Mega Minior   500  60    100      60       100         60   120
## 909             Komala   480  65    115      65        75         95    65
## 910         Turtonator   485  60     78     135        91         85    36
## 911         Togedemaru   435  65     98      63        40         73    96
## 912            Mimikyu   476  55     90      80        50        105    96
## 913            Bruxish   475  68    105      70        70         70    92
## 914             Drampa   485  78     60      85       135         91    36
## 915           Dhelmise   517  70    131     100        86         90    40
## 916           Jangmo-o   300  45     55      65        45         45    45
## 917           Hakamo-o   420  55     75      90        65         70    65
## 918            Kommo-o   600  75    110     125       100        105    85
## 919          Tapu Koko   570  70    115      85        95         75   130
## 920          Tapu Lele   570  70     85      75       130        115    95
## 921          Tapu Bulu   570  70    130     115        85         95    75
## 922          Tapu Fini   570  70     75     115        95        130    85
## 923             Cosmog   200  43     29      31        29         31    37
## 924            Cosmoem   400  43     29     131        29        131    37
## 925           Solgaleo   680 137    137     107       113         89    97
## 926             Lunala   680 137    113      89       137        107    97
## 927           Nihilego   570 109     53      47       127        131   103
## 928           Buzzwole   570 107    139     139        53         53    79
## 929          Pheromosa   570  71    137      37       137         37   151
## 930          Xurkitree   570  83     89      71       173         71    83
## 931         Celesteela   570  97    101     103       107        101    61
## 932            Kartana   570  59    181     131        59         31   109
## 933           Guzzlord   570 223    101      53        97         53    43
## 934           Necrozma   600  97    107     101       127         89    79
## 935      Mega Necrozma   680  97    157     127       113        109    77
## 936    Mega Necrozma X   680  97    113     109       157        127    77
## 937    Mega Necrozma X   754  97    167      97       167         97   129
## 938           Magearna   600  80     95     115       130        115    65
## 939          Marshadow   600  90    125      80        90         90   125
## 940            Poipole   420  67     73      67        73         67    73
## 941          Naganadel   540  73     73      73       127         73   121
## 942          Stakataka   570  61    131     211        53        101    13
## 943        Blacephalon   570  53    127      53       151         79   107
## 944            Zeraora   600  88    112      75       102         80   143
## 945             Meltan   300  46     65      65        55         35    34
## 946           Melmetal   600 135    143     143        80         65    34
## 947            Grookey   310  50     65      50        40         40    65
## 948           Thwackey   420  70     85      70        55         60    80
## 949          Rillaboom   530 100    125      90        60         70    85
## 950          Scorbunny   310  50     71      40        40         40    69
## 951             Raboot   420  65     86      60        55         60    94
## 952          Cinderace   530  80    116      75        65         75   119
## 953             Sobble   310  50     40      40        70         40    70
## 954           Drizzile   420  65     60      55        95         55    90
## 955           Inteleon   530  70     85      65       125         65   120
## 956            Skwovet   275  70     55      55        35         35    25
## 957           Greedent   460 120     95      95        55         75    20
## 958           Rookidee   245  38     47      35        33         35    57
## 959        Corvisquire   365  68     67      55        43         55    77
## 960        Corviknight   495  98     87     105        53         85    67
## 961            Blipbug   180  25     20      20        25         45    45
## 962            Dottler   335  50     35      80        50         90    30
## 963           Orbeetle   505  60     45     110        80        120    90
## 964             Nickit   245  40     28      28        47         52    50
## 965            Thievul   455  70     58      58        87         92    90
## 966         Gossifleur   250  40     40      60        40         60    10
## 967           Eldegoss   460  60     50      90        80        120    60
## 968             Wooloo   270  42     40      55        40         45    48
## 969            Dubwool   490  72     80     100        60         90    88
## 970            Chewtle   284  50     64      50        38         38    44
## 971            Drednaw   485  90    115      90        48         68    74
## 972             Yamper   270  59     45      50        40         50    26
## 973            Boltund   490  69     90      60        90         60   121
## 974           Rolycoly   240  30     40      50        40         50    30
## 975             Carkol   410  80     60      90        60         70    50
## 976          Coalossal   510 110     80     120        80         90    30
## 977             Applin   260  40     40      80        40         40    20
## 978            Flapple   485  70    110      80        95         60    70
## 979           Appletun   485 110     85      80       100         80    30
## 980          Silicobra   315  52     57      75        35         50    46
## 981         Sandaconda   510  72    107     125        65         70    71
## 982          Cramorant   475  70     85      55        85         95    85
## 983           Arrokuda   280  41     63      40        40         30    66
## 984        Barraskewda   490  61    123      60        60         50   136
## 985              Toxel   242  40     38      35        54         35    40
## 986         Toxtricity   502  75     98      70       114         70    75
## 987    Mega Toxtricity   502  75     98      70       114         70    75
## 988         Sizzlipede   305  50     65      45        50         50    45
## 989        Centiskorch   525 100    115      65        90         90    65
## 990          Clobbopus   310  50     68      60        50         50    32
## 991          Grapploct   480  80    118      90        70         80    42
## 992           Sinistea   308  40     45      45        74         54    50
## 993        Polteageist   508  60     65      65       134        114    70
## 994            Hatenna   265  42     30      45        56         53    39
## 995            Hattrem   370  57     40      65        86         73    49
## 996          Hatterene   510  57     90      95       136        103    29
## 997           Impidimp   265  45     45      30        55         40    50
## 998            Morgrem   370  65     60      45        75         55    70
## 999         Grimmsnarl   510  95    120      65        95         75    60
## 1000         Obstagoon   520  93     90     101        60         81    95
## 1001        Perrserker   440  70    110     100        50         60    50
## 1002           Cursola   510  60     95      50       145        130    30
## 1003        Sirfetch'd   507  62    135      95        68         82    65
## 1004          Mr. Rime   520  80     85      75       110        100    70
## 1005         Runerigus   483  58     95     145        50        105    30
## 1006           Milcery   270  45     40      40        50         61    34
## 1007          Alcremie   495  65     60      75       110        121    64
## 1008           Falinks   470  65    100     100        70         60    75
## 1009        Pincurchin   435  48    101      95        91         85    15
## 1010              Snom   185  30     25      35        45         30    20
## 1011          Frosmoth   475  70     65      60       125         90    65
## 1012       Stonjourner   470 100    125     135        20         20    70
## 1013            Eiscue   470  75     80     110        65         90    50
## 1014       Mega Eiscue   470  75     80      70        65         50   130
## 1015          Indeedee   475  60     65      55       105         95    95
## 1016     Mega Indeedee   475  70     55      65        95        105    85
## 1017           Morpeko   436  58     95      58        70         58    97
## 1018      Mega Morpeko   436  58     95      58        70         58    97
## 1019            Cufant   330  72     80      49        40         49    40
## 1020        Copperajah   500 122    130      69        80         69    30
## 1021         Dracozolt   505  90    100      90        80         70    75
## 1022         Arctozolt   505  90    100      90        90         80    55
## 1023         Dracovish   505  90     90     100        70         80    75
## 1024         Arctovish   505  90     90     100        80         90    55
## 1025         Duraludon   535  70     95     115       120         50    85
## 1026            Dreepy   270  28     60      30        40         30    82
## 1027          Drakloak   410  68     80      50        60         50   102
## 1028         Dragapult   600  88    120      75       100         75   142
## 1029            Zacian   720  92    170     115        80        115   148
## 1030       Mega Zacian   670  92    130     115        80        115   138
## 1031         Zamazenta   720  92    130     145        80        145   128
## 1032    Mega Zamazenta   670  92    130     115        80        115   138
## 1033         Eternatus   690 140     85      95       145         95   130
## 1034    Mega Eternatus  1125 255    115     250       125        250   130
## 1035             Kubfu   385  60     90      60        53         50    72
## 1036           Urshifu   550 100    130     100        63         60    97
## 1037      Mega Urshifu   550 100    130     100        63         60    97
## 1038            Zarude   600 105    120     105        70         95   105
## 1039         Regieleki   580  80    100      50       100         50   200
## 1040         Regidrago   580 200    100      50       100         50    80
## 1041         Glastrier   580 100    145     130        65        110    30
## 1042         Spectrier   580 100     65      60       145         80   130
## 1043           Calyrex   500 100     80      80        80         80    80
## 1044      Mega Calyrex   680 100    165     150        85        130    50
## 1045    Mega Calyrex X   680 100     85      80       165        100   150
length(d2)
## [1] 8
head(d2)
tail(d2)
str(d2)
## 'data.frame':    1045 obs. of  8 variables:
##  $ Name      : chr  "Bulbasaur" "Ivysaur" "Venusaur" "Mega Venusaur" ...
##  $ Total     : int  318 405 525 625 309 405 534 634 634 314 ...
##  $ HP        : int  45 60 80 80 39 58 78 78 78 44 ...
##  $ Attack    : int  49 62 82 100 52 64 84 130 104 48 ...
##  $ Defence   : int  49 63 83 123 43 58 78 111 78 65 ...
##  $ Sp_attack : int  65 80 100 122 60 80 109 130 159 50 ...
##  $ Sp_defence: int  65 80 100 120 50 65 85 85 115 64 ...
##  $ Speed     : int  45 60 80 80 65 80 100 100 100 43 ...
d2$Sp_defence
##    [1]  65  80 100 120  50  65  85  85 115  64  80 105 115  20  25  80  20  25
##   [19]  80  80  35  50  70  80  35  35  70  80  31  61  54  79  50  60  80  85
##   [37]  30  35  55  65  40  55  85  40  55  75  65  90  65  65 100 100  25  50
##   [55]  40  75  65  75  90  55  80  55  75  45  45  70  70  40  40  40  65  65
##   [73]  50  80  45  70  50  80  40  50  90  55  70  95 105  35  60  85  30  45
##   [91]  70 100 120  30  30  45  45  65  65  65  65  80  80  40  40  80  80  70
##  [109]  55  70  62  62  35  60  70  95  50  50 100 100  25  45  35  55  75  95
##  [127]  45  90 115  25  50  55  80  45  75  75  50  80  80 110 110  75  45  70
##  [145]  70  30  45 105  40  80 100  25  45  50  80  55  85 120  90  80  95  85
##  [163]  85  70  90  70  20 100 130  95  48  65  85  95  95 110  75  55  70  45
##  [181]  70  75  95 110 125 100  90  90  85 125  50  70 100  90 100 120 100  65
##  [199]  80 100  50  65  85  48  63  83  45  55  56  96  80 110  40  70  80  56
##  [217]  76  35  55  20  65 105  45  70  45  60  90 110 100  50  80  65 100  55
##  [235]  65  95  55  30  85  45  25  65  95 130  42 110 110  85  48  58  65  35
##  [253]  60  65  65  65  95  40  60  55  80 100 230  95 105  75  50  75  40  80
##  [271]  30  60  95 100  35  75  45 140  70  50  80  90  95  40  60  95  65  45
##  [289]  35 110  65  55  55  70 135 100  75 115  50  70 100 120 154 154 100  55
##  [307]  65  85  85  50  60  70  80  50  70  90 110  30  60  41  41  61  61  30
##  [325]  25  50  25  90  50  70 100  30  40  60  30  50  30  70  35  55 115 135
##  [343]  52  82  60  60  35  55  65  30  50  30  23  43  73  30  60  40  90  35
##  [361]  55  65 115  55  95  40  50  60  80  55  75  85  40  60  80  75  85  85
##  [379]  85  80  53  83  20  40  65  35  45  45  75 105  70  80 110  60  45  50
##  [397]  80  40  60  75 105 105  60  60  85  65  41  71  35  55  70 120  87 107
##  [415]  50  80  55 125  70  70  70  70 120  33  63  83  90 130  87  90  60  60
##  [433]  48  50  80  80  50  70  90  55  75  75  65  65  30  50  80  90  60  80
##  [451]  90 110 100 200 150 130 150 110 120 140 160  90  90  90 100 100  50  20
##  [469] 160  90  55  65  85  44  52  71  56  76 101  30  40  60  40  60  41  51
##  [487]  34  49  79  70 105  30  50  88 138  45 105  85  95  50  42 102  90  30
##  [505]  50  53  78  62  82  66  44  54  56  96  96 105  52  37  59  50  41  61
##  [523]  86 116  45  90  65  42 108  45  55  85  95  85  40  70  70  42  72  55
##  [541]  75  40  65  72  61  86 120  60  85 105  85  90  95  55  50  85  95 115
##  [559]  56  65  95  75  60  75 115 115 150 135  70  77 107 107 107 107 107 130
##  [577] 105  70 100 120 106 110 120 100 130  80 100  90 100  75 120 100  55  75
##  [595]  95  45  55  65  45  60  70  39  69  45  65  90  37  50  48  63  48  63
##  [613]  48  63  55  95  30  42  55  32  63  25  40  80  43  55  45  65  86 126
##  [631]  35  50  65  40  55  75  85  75  60  80  80  39  79  69  50  75  50  75
##  [649]  55  55  35  45  70  45  45  55 105  55  55  67  35  75  70 115  80  65
##  [667]  65 105  45  65  45  65  62  82  40  60  40  60  65  85 110  50  60  85
##  [685]  50  63  60  75  95  50  70  60  45 105  55  80  85 105  45  50  60  86
##  [703] 116  60  85  85  40  70  80  55  95  55  60  90  40  50  70  40  80 135
##  [721]  65  60  99  84  50  60  90  50  80  40  70  95  50  75  65  95  66  48
##  [739]  50  70  90  55 105  72  90 129  80  90  80  80 120 100  80  80  90  90
##  [757] 100  90  90 128  77  95  45  58  75  60  70 100  44  56  71  71  36  77
##  [775]  38  52  69  25  30  50  54  66  79  98 154  57  81  48  71  90  60  81
##  [793]  81  37  49 140  50  65  89  57  75  46  75  56  86  60 123  63  89  43
##  [811]  94  45  59  63  92 130  63  67 150  75 113 150  87  60  82  55  55  55
##  [829]  55  75  75  75  75  35  46  40  80  98  98  95  85  95 150 110 130 130
##  [847]  90  50  70 100  40  50  90  56  81 116  30  50  75  30  60  45  75  75
##  [865]  47  67  70  70  70  70  40  70  40  40  65  75  65  25 135  52 142  55
##  [883]  85  72 132  35  90  75 100  40  60  50  60  38  48  98 110 110  60  30
##  [901]  90  45  75 130  95  95 100  60  95  85  73 105  70  91  90  45  70 105
##  [919]  75 115  95 130  31 131  89 107 131  53  37  71 101  31  53  89 109 127
##  [937]  97 115  90  67  73 101  79  80  35  65  40  60  70  40  60  75  40  55
##  [955]  65  35  75  35  55  85  45  90 120  52  92  60 120  45  90  38  68  50
##  [973]  60  50  70  90  40  60  80  50  70  95  30  50  35  70  70  50  90  50
##  [991]  80  54 114  53  73 103  40  55  75  81  60 130  82 100 105  61 121  60
## [1009]  85  30  90  20  90  50  95 105  58  58  49  69  70  80  80  90  50  30
## [1027]  50  75 115 115 145 115  95 250  50  60  60  95  50  50 110  80  80 130
## [1045] 100
d2$d2
## NULL
cbind(d2)
dimnames(d2)
## [[1]]
##    [1] "1"    "2"    "3"    "4"    "5"    "6"    "7"    "8"    "9"    "10"  
##   [11] "11"   "12"   "13"   "14"   "15"   "16"   "17"   "18"   "19"   "20"  
##   [21] "21"   "22"   "23"   "24"   "25"   "26"   "27"   "28"   "29"   "30"  
##   [31] "31"   "32"   "33"   "34"   "35"   "36"   "37"   "38"   "39"   "40"  
##   [41] "41"   "42"   "43"   "44"   "45"   "46"   "47"   "48"   "49"   "50"  
##   [51] "51"   "52"   "53"   "54"   "55"   "56"   "57"   "58"   "59"   "60"  
##   [61] "61"   "62"   "63"   "64"   "65"   "66"   "67"   "68"   "69"   "70"  
##   [71] "71"   "72"   "73"   "74"   "75"   "76"   "77"   "78"   "79"   "80"  
##   [81] "81"   "82"   "83"   "84"   "85"   "86"   "87"   "88"   "89"   "90"  
##   [91] "91"   "92"   "93"   "94"   "95"   "96"   "97"   "98"   "99"   "100" 
##  [101] "101"  "102"  "103"  "104"  "105"  "106"  "107"  "108"  "109"  "110" 
##  [111] "111"  "112"  "113"  "114"  "115"  "116"  "117"  "118"  "119"  "120" 
##  [121] "121"  "122"  "123"  "124"  "125"  "126"  "127"  "128"  "129"  "130" 
##  [131] "131"  "132"  "133"  "134"  "135"  "136"  "137"  "138"  "139"  "140" 
##  [141] "141"  "142"  "143"  "144"  "145"  "146"  "147"  "148"  "149"  "150" 
##  [151] "151"  "152"  "153"  "154"  "155"  "156"  "157"  "158"  "159"  "160" 
##  [161] "161"  "162"  "163"  "164"  "165"  "166"  "167"  "168"  "169"  "170" 
##  [171] "171"  "172"  "173"  "174"  "175"  "176"  "177"  "178"  "179"  "180" 
##  [181] "181"  "182"  "183"  "184"  "185"  "186"  "187"  "188"  "189"  "190" 
##  [191] "191"  "192"  "193"  "194"  "195"  "196"  "197"  "198"  "199"  "200" 
##  [201] "201"  "202"  "203"  "204"  "205"  "206"  "207"  "208"  "209"  "210" 
##  [211] "211"  "212"  "213"  "214"  "215"  "216"  "217"  "218"  "219"  "220" 
##  [221] "221"  "222"  "223"  "224"  "225"  "226"  "227"  "228"  "229"  "230" 
##  [231] "231"  "232"  "233"  "234"  "235"  "236"  "237"  "238"  "239"  "240" 
##  [241] "241"  "242"  "243"  "244"  "245"  "246"  "247"  "248"  "249"  "250" 
##  [251] "251"  "252"  "253"  "254"  "255"  "256"  "257"  "258"  "259"  "260" 
##  [261] "261"  "262"  "263"  "264"  "265"  "266"  "267"  "268"  "269"  "270" 
##  [271] "271"  "272"  "273"  "274"  "275"  "276"  "277"  "278"  "279"  "280" 
##  [281] "281"  "282"  "283"  "284"  "285"  "286"  "287"  "288"  "289"  "290" 
##  [291] "291"  "292"  "293"  "294"  "295"  "296"  "297"  "298"  "299"  "300" 
##  [301] "301"  "302"  "303"  "304"  "305"  "306"  "307"  "308"  "309"  "310" 
##  [311] "311"  "312"  "313"  "314"  "315"  "316"  "317"  "318"  "319"  "320" 
##  [321] "321"  "322"  "323"  "324"  "325"  "326"  "327"  "328"  "329"  "330" 
##  [331] "331"  "332"  "333"  "334"  "335"  "336"  "337"  "338"  "339"  "340" 
##  [341] "341"  "342"  "343"  "344"  "345"  "346"  "347"  "348"  "349"  "350" 
##  [351] "351"  "352"  "353"  "354"  "355"  "356"  "357"  "358"  "359"  "360" 
##  [361] "361"  "362"  "363"  "364"  "365"  "366"  "367"  "368"  "369"  "370" 
##  [371] "371"  "372"  "373"  "374"  "375"  "376"  "377"  "378"  "379"  "380" 
##  [381] "381"  "382"  "383"  "384"  "385"  "386"  "387"  "388"  "389"  "390" 
##  [391] "391"  "392"  "393"  "394"  "395"  "396"  "397"  "398"  "399"  "400" 
##  [401] "401"  "402"  "403"  "404"  "405"  "406"  "407"  "408"  "409"  "410" 
##  [411] "411"  "412"  "413"  "414"  "415"  "416"  "417"  "418"  "419"  "420" 
##  [421] "421"  "422"  "423"  "424"  "425"  "426"  "427"  "428"  "429"  "430" 
##  [431] "431"  "432"  "433"  "434"  "435"  "436"  "437"  "438"  "439"  "440" 
##  [441] "441"  "442"  "443"  "444"  "445"  "446"  "447"  "448"  "449"  "450" 
##  [451] "451"  "452"  "453"  "454"  "455"  "456"  "457"  "458"  "459"  "460" 
##  [461] "461"  "462"  "463"  "464"  "465"  "466"  "467"  "468"  "469"  "470" 
##  [471] "471"  "472"  "473"  "474"  "475"  "476"  "477"  "478"  "479"  "480" 
##  [481] "481"  "482"  "483"  "484"  "485"  "486"  "487"  "488"  "489"  "490" 
##  [491] "491"  "492"  "493"  "494"  "495"  "496"  "497"  "498"  "499"  "500" 
##  [501] "501"  "502"  "503"  "504"  "505"  "506"  "507"  "508"  "509"  "510" 
##  [511] "511"  "512"  "513"  "514"  "515"  "516"  "517"  "518"  "519"  "520" 
##  [521] "521"  "522"  "523"  "524"  "525"  "526"  "527"  "528"  "529"  "530" 
##  [531] "531"  "532"  "533"  "534"  "535"  "536"  "537"  "538"  "539"  "540" 
##  [541] "541"  "542"  "543"  "544"  "545"  "546"  "547"  "548"  "549"  "550" 
##  [551] "551"  "552"  "553"  "554"  "555"  "556"  "557"  "558"  "559"  "560" 
##  [561] "561"  "562"  "563"  "564"  "565"  "566"  "567"  "568"  "569"  "570" 
##  [571] "571"  "572"  "573"  "574"  "575"  "576"  "577"  "578"  "579"  "580" 
##  [581] "581"  "582"  "583"  "584"  "585"  "586"  "587"  "588"  "589"  "590" 
##  [591] "591"  "592"  "593"  "594"  "595"  "596"  "597"  "598"  "599"  "600" 
##  [601] "601"  "602"  "603"  "604"  "605"  "606"  "607"  "608"  "609"  "610" 
##  [611] "611"  "612"  "613"  "614"  "615"  "616"  "617"  "618"  "619"  "620" 
##  [621] "621"  "622"  "623"  "624"  "625"  "626"  "627"  "628"  "629"  "630" 
##  [631] "631"  "632"  "633"  "634"  "635"  "636"  "637"  "638"  "639"  "640" 
##  [641] "641"  "642"  "643"  "644"  "645"  "646"  "647"  "648"  "649"  "650" 
##  [651] "651"  "652"  "653"  "654"  "655"  "656"  "657"  "658"  "659"  "660" 
##  [661] "661"  "662"  "663"  "664"  "665"  "666"  "667"  "668"  "669"  "670" 
##  [671] "671"  "672"  "673"  "674"  "675"  "676"  "677"  "678"  "679"  "680" 
##  [681] "681"  "682"  "683"  "684"  "685"  "686"  "687"  "688"  "689"  "690" 
##  [691] "691"  "692"  "693"  "694"  "695"  "696"  "697"  "698"  "699"  "700" 
##  [701] "701"  "702"  "703"  "704"  "705"  "706"  "707"  "708"  "709"  "710" 
##  [711] "711"  "712"  "713"  "714"  "715"  "716"  "717"  "718"  "719"  "720" 
##  [721] "721"  "722"  "723"  "724"  "725"  "726"  "727"  "728"  "729"  "730" 
##  [731] "731"  "732"  "733"  "734"  "735"  "736"  "737"  "738"  "739"  "740" 
##  [741] "741"  "742"  "743"  "744"  "745"  "746"  "747"  "748"  "749"  "750" 
##  [751] "751"  "752"  "753"  "754"  "755"  "756"  "757"  "758"  "759"  "760" 
##  [761] "761"  "762"  "763"  "764"  "765"  "766"  "767"  "768"  "769"  "770" 
##  [771] "771"  "772"  "773"  "774"  "775"  "776"  "777"  "778"  "779"  "780" 
##  [781] "781"  "782"  "783"  "784"  "785"  "786"  "787"  "788"  "789"  "790" 
##  [791] "791"  "792"  "793"  "794"  "795"  "796"  "797"  "798"  "799"  "800" 
##  [801] "801"  "802"  "803"  "804"  "805"  "806"  "807"  "808"  "809"  "810" 
##  [811] "811"  "812"  "813"  "814"  "815"  "816"  "817"  "818"  "819"  "820" 
##  [821] "821"  "822"  "823"  "824"  "825"  "826"  "827"  "828"  "829"  "830" 
##  [831] "831"  "832"  "833"  "834"  "835"  "836"  "837"  "838"  "839"  "840" 
##  [841] "841"  "842"  "843"  "844"  "845"  "846"  "847"  "848"  "849"  "850" 
##  [851] "851"  "852"  "853"  "854"  "855"  "856"  "857"  "858"  "859"  "860" 
##  [861] "861"  "862"  "863"  "864"  "865"  "866"  "867"  "868"  "869"  "870" 
##  [871] "871"  "872"  "873"  "874"  "875"  "876"  "877"  "878"  "879"  "880" 
##  [881] "881"  "882"  "883"  "884"  "885"  "886"  "887"  "888"  "889"  "890" 
##  [891] "891"  "892"  "893"  "894"  "895"  "896"  "897"  "898"  "899"  "900" 
##  [901] "901"  "902"  "903"  "904"  "905"  "906"  "907"  "908"  "909"  "910" 
##  [911] "911"  "912"  "913"  "914"  "915"  "916"  "917"  "918"  "919"  "920" 
##  [921] "921"  "922"  "923"  "924"  "925"  "926"  "927"  "928"  "929"  "930" 
##  [931] "931"  "932"  "933"  "934"  "935"  "936"  "937"  "938"  "939"  "940" 
##  [941] "941"  "942"  "943"  "944"  "945"  "946"  "947"  "948"  "949"  "950" 
##  [951] "951"  "952"  "953"  "954"  "955"  "956"  "957"  "958"  "959"  "960" 
##  [961] "961"  "962"  "963"  "964"  "965"  "966"  "967"  "968"  "969"  "970" 
##  [971] "971"  "972"  "973"  "974"  "975"  "976"  "977"  "978"  "979"  "980" 
##  [981] "981"  "982"  "983"  "984"  "985"  "986"  "987"  "988"  "989"  "990" 
##  [991] "991"  "992"  "993"  "994"  "995"  "996"  "997"  "998"  "999"  "1000"
## [1001] "1001" "1002" "1003" "1004" "1005" "1006" "1007" "1008" "1009" "1010"
## [1011] "1011" "1012" "1013" "1014" "1015" "1016" "1017" "1018" "1019" "1020"
## [1021] "1021" "1022" "1023" "1024" "1025" "1026" "1027" "1028" "1029" "1030"
## [1031] "1031" "1032" "1033" "1034" "1035" "1036" "1037" "1038" "1039" "1040"
## [1041] "1041" "1042" "1043" "1044" "1045"
## 
## [[2]]
## [1] "Name"       "Total"      "HP"         "Attack"     "Defence"   
## [6] "Sp_attack"  "Sp_defence" "Speed"
names(d2)
## [1] "Name"       "Total"      "HP"         "Attack"     "Defence"   
## [6] "Sp_attack"  "Sp_defence" "Speed"
mean(d2$Attack)
## [1] 80.46699
max(d2$HP)
## [1] 255
quantile(d2$Sp_attack)
##   0%  25%  50%  75% 100% 
##   10   50   65   95  194
fivenum(d2$Defence)
## [1]   5  50  70  90 250

Cumulative statistics:

cumsum(d2$HP)
##    [1]    45   105   185   265   304   362   440   518   596   640   699   778
##   [13]   857   902   952  1012  1052  1097  1162  1227  1267  1330  1413  1496
##   [25]  1526  1556  1611  1686  1726  1791  1826  1886  1921  1966  2026  2086
##   [37]  2136  2186  2261  2336  2391  2461  2551  2597  2658  2739  2809  2904
##   [49]  2942  2980  3053  3126  3241  3381  3421  3496  3541  3601  3676  3711
##   [61]  3771  3831  3901  3911  3921  3956  3991  4031  4071  4121  4186  4251
##   [73]  4301  4381  4421  4486  4541  4631  4671  4736  4826  4851  4891  4946
##   [85]  5001  5071  5151  5241  5291  5356  5436  5476  5556  5596  5636  5691
##   [97]  5746  5826  5906  5956  6006  6071  6136  6226  6316  6411  6506  6601
##  [109]  6626  6676  6728  6780  6815  6875  6940  7030  7110  7190  7295  7400
##  [121]  7430  7480  7510  7555  7615  7675  7710  7770  7855  7885  7940  7980
##  [133]  8040  8100  8195  8290  8340  8400  8460  8510  8560  8650  8690  8755
##  [145]  8820  8900  9005  9255  9320  9425  9530  9560  9615  9660  9740  9770
##  [157]  9830  9870  9920  9990 10055 10120 10185 10250 10315 10390 10410 10505
##  [169] 10600 10730 10778 10833 10898 11028 11093 11158 11223 11258 11328 11358
##  [181] 11418 11498 11578 11738 11828 11918 12008 12098 12188 12278 12319 12380
##  [193] 12471 12577 12683 12789 12889 12934 12994 13074 13113 13171 13249 13299
##  [205] 13364 13449 13484 13569 13629 13729 13769 13824 13864 13934 14019 14094
##  [217] 14219 14239 14289 14379 14414 14469 14509 14574 14629 14699 14789 14879
##  [229] 14954 15024 15124 15194 15284 15319 15374 15449 15504 15534 15609 15674
##  [241] 15729 15824 15889 15984 16044 16139 16234 16294 16342 16532 16602 16652
##  [253] 16727 16827 16892 16967 17042 17102 17192 17257 17327 17397 17417 17497
##  [265] 17577 17632 17692 17782 17822 17882 17932 18032 18097 18157 18192 18267
##  [277] 18312 18397 18462 18507 18582 18657 18732 18822 18912 18997 19070 19125
##  [289] 19160 19210 19255 19300 19345 19440 19695 19785 19900 20000 20050 20120
##  [301] 20220 20320 20426 20532 20632 20672 20722 20792 20862 20907 20967 21047
##  [313] 21127 21177 21247 21347 21447 21482 21552 21590 21628 21706 21784 21829
##  [325] 21879 21939 21989 22049 22089 22149 22229 22269 22339 22429 22469 22529
##  [337] 22569 22629 22657 22695 22763 22831 22871 22941 23001 23061 23121 23201
##  [349] 23351 23382 23443 23444 23508 23592 23696 23768 23912 23962 23992 24042
##  [361] 24112 24162 24212 24262 24312 24362 24422 24492 24562 24592 24652 24712
##  [373] 24752 24822 24892 24952 25012 25077 25142 25192 25262 25362 25407 25477
##  [385] 25547 25677 25847 25907 25977 26047 26117 26177 26257 26317 26362 26412
##  [397] 26492 26542 26612 26657 26732 26807 26880 26953 27043 27133 27183 27293
##  [409] 27336 27399 27439 27499 27565 27651 27696 27771 27791 27886 27956 28026
##  [421] 28096 28166 28226 28270 28334 28398 28418 28458 28557 28632 28697 28762
##  [433] 28857 28907 28987 29067 29137 29227 29337 29372 29427 29482 29582 29625
##  [445] 29670 29735 29830 29925 29965 30025 30105 30185 30265 30345 30425 30505
##  [457] 30585 30665 30745 30845 30945 31045 31145 31250 31355 31455 31505 31555
##  [469] 31605 31655 31710 31785 31880 31924 31988 32064 32117 32181 32265 32305
##  [481] 32360 32445 32504 32583 32620 32697 32742 32802 32882 32922 32982 33049
##  [493] 33146 33176 33236 33276 33336 33396 33456 33526 33556 33626 33686 33741
##  [505] 33826 33871 33941 34017 34128 34203 34293 34443 34498 34563 34628 34688
##  [517] 34788 34837 34908 34953 35016 35119 35176 35243 35293 35313 35413 35489
##  [529] 35539 35597 35665 35773 35881 36016 36056 36126 36196 36264 36372 36412
##  [541] 36482 36530 36613 36687 36736 36805 36850 36910 37000 37090 37160 37230
##  [553] 37340 37455 37555 37630 37705 37790 37876 37941 38006 38081 38191 38276
##  [565] 38344 38412 38472 38517 38587 38637 38687 38737 38787 38837 38887 38962
##  [577] 39042 39117 39217 39307 39398 39508 39658 39808 39928 40008 40108 40178
##  [589] 40278 40378 40498 40598 40643 40703 40778 40843 40933 41043 41098 41173
##  [601] 41268 41313 41373 41418 41483 41568 41609 41673 41723 41798 41848 41923
##  [613] 41973 42048 42124 42240 42290 42352 42432 42477 42552 42607 42677 42762
##  [625] 42827 42894 42954 43064 43167 43270 43345 43430 43535 43585 43660 43765
##  [637] 43885 43960 44005 44060 44135 44165 44205 44265 44305 44365 44410 44480
##  [649] 44550 44620 44670 44730 44825 44895 44965 45070 45175 45280 45385 45460
##  [661] 45510 45580 45630 45695 45767 45805 45843 45901 45955 46029 46084 46159
##  [673] 46209 46289 46329 46389 46444 46519 46564 46624 46694 46739 46804 46914
##  [685] 46976 47051 47087 47138 47209 47269 47349 47404 47454 47524 47593 47707
##  [697] 47762 47862 48027 48077 48147 48191 48265 48305 48365 48425 48460 48525
##  [709] 48610 48665 48740 48790 48850 48910 48956 49022 49098 49153 49248 49328
##  [721] 49378 49458 49567 49676 49721 49786 49863 49922 50011 50056 50121 50216
##  [733] 50286 50386 50456 50566 50651 50709 50761 50833 50925 50980 51065 51156
##  [745] 51247 51338 51417 51496 51575 51654 51754 51854 51943 52032 52157 52282
##  [757] 52407 52498 52589 52689 52789 52860 52916 52977 53065 53105 53164 53239
##  [769] 53280 53334 53406 53478 53516 53601 53646 53708 53786 53824 53869 53949
##  [781] 54011 54097 54141 54195 54273 54339 54462 54529 54624 54699 54761 54835
##  [793] 54909 54954 55013 55073 55133 55211 55312 55374 55456 55509 55595 55637
##  [805] 55709 55759 55824 55874 55945 55989 56051 56109 56191 56268 56391 56486
##  [817] 56564 56631 56681 56726 56794 56884 56941 56984 57069 57118 57162 57216
##  [829] 57275 57340 57395 57470 57555 57610 57705 57745 57830 57956 58082 58190
##  [841] 58244 58460 58510 58560 58640 58720 58800 58868 58946 59024 59069 59134
##  [853] 59229 59279 59339 59419 59454 59509 59589 59637 59725 59772 59829 59906
##  [865] 59953 60050 60125 60200 60275 60350 60390 60450 60495 60540 60615 60700
##  [877] 60775 60820 60865 60915 60965 61035 61135 61173 61241 61281 61351 61391
##  [889] 61451 61499 61567 61637 61757 61799 61851 61923 61974 62064 62164 62189
##  [901] 62264 62319 62404 62459 62554 62649 62709 62769 62834 62894 62959 63014
##  [913] 63082 63160 63230 63275 63330 63405 63475 63545 63615 63685 63728 63771
##  [925] 63908 64045 64154 64261 64332 64415 64512 64571 64794 64891 64988 65085
##  [937] 65182 65262 65352 65419 65492 65553 65606 65694 65740 65875 65925 65995
##  [949] 66095 66145 66210 66290 66340 66405 66475 66545 66665 66703 66771 66869
##  [961] 66894 66944 67004 67044 67114 67154 67214 67256 67328 67378 67468 67527
##  [973] 67596 67626 67706 67816 67856 67926 68036 68088 68160 68230 68271 68332
##  [985] 68372 68447 68522 68572 68672 68722 68802 68842 68902 68944 69001 69058
##  [997] 69103 69168 69263 69356 69426 69486 69548 69628 69686 69731 69796 69861
## [1009] 69909 69939 70009 70109 70184 70259 70319 70389 70447 70505 70577 70699
## [1021] 70789 70879 70969 71059 71129 71157 71225 71313 71405 71497 71589 71681
## [1033] 71821 72076 72136 72236 72336 72441 72521 72721 72821 72921 73021 73121
## [1045] 73221
head(cumsum(d2$Total))
## [1]  318  723 1248 1873 2182 2587
head(cumsum(d2$Attack))
## [1]  49 111 193 293 345 409
head(cumsum(d2$Defence))
## [1]  49 112 195 318 361 419
head(cumsum(d2$Sp_attack))
## [1]  65 145 245 367 427 507
head(cumsum(d2$Sp_defence))
## [1]  65 145 245 365 415 480
head(cumsum(d2$Speed))
## [1]  45 105 185 265 330 410
cummax(d2$HP)
##    [1]  45  60  80  80  80  80  80  80  80  80  80  80  80  80  80  80  80  80
##   [19]  80  80  80  80  83  83  83  83  83  83  83  83  83  83  83  83  83  83
##   [37]  83  83  83  83  83  83  90  90  90  90  90  95  95  95  95  95 115 140
##   [55] 140 140 140 140 140 140 140 140 140 140 140 140 140 140 140 140 140 140
##   [73] 140 140 140 140 140 140 140 140 140 140 140 140 140 140 140 140 140 140
##   [91] 140 140 140 140 140 140 140 140 140 140 140 140 140 140 140 140 140 140
##  [109] 140 140 140 140 140 140 140 140 140 140 140 140 140 140 140 140 140 140
##  [127] 140 140 140 140 140 140 140 140 140 140 140 140 140 140 140 140 140 140
##  [145] 140 140 140 250 250 250 250 250 250 250 250 250 250 250 250 250 250 250
##  [163] 250 250 250 250 250 250 250 250 250 250 250 250 250 250 250 250 250 250
##  [181] 250 250 250 250 250 250 250 250 250 250 250 250 250 250 250 250 250 250
##  [199] 250 250 250 250 250 250 250 250 250 250 250 250 250 250 250 250 250 250
##  [217] 250 250 250 250 250 250 250 250 250 250 250 250 250 250 250 250 250 250
##  [235] 250 250 250 250 250 250 250 250 250 250 250 250 250 250 250 250 250 250
##  [253] 250 250 250 250 250 250 250 250 250 250 250 250 250 250 250 250 250 250
##  [271] 250 250 250 250 250 250 250 250 250 250 250 250 250 250 250 250 250 250
##  [289] 250 250 250 250 250 250 255 255 255 255 255 255 255 255 255 255 255 255
##  [307] 255 255 255 255 255 255 255 255 255 255 255 255 255 255 255 255 255 255
##  [325] 255 255 255 255 255 255 255 255 255 255 255 255 255 255 255 255 255 255
##  [343] 255 255 255 255 255 255 255 255 255 255 255 255 255 255 255 255 255 255
##  [361] 255 255 255 255 255 255 255 255 255 255 255 255 255 255 255 255 255 255
##  [379] 255 255 255 255 255 255 255 255 255 255 255 255 255 255 255 255 255 255
##  [397] 255 255 255 255 255 255 255 255 255 255 255 255 255 255 255 255 255 255
##  [415] 255 255 255 255 255 255 255 255 255 255 255 255 255 255 255 255 255 255
##  [433] 255 255 255 255 255 255 255 255 255 255 255 255 255 255 255 255 255 255
##  [451] 255 255 255 255 255 255 255 255 255 255 255 255 255 255 255 255 255 255
##  [469] 255 255 255 255 255 255 255 255 255 255 255 255 255 255 255 255 255 255
##  [487] 255 255 255 255 255 255 255 255 255 255 255 255 255 255 255 255 255 255
##  [505] 255 255 255 255 255 255 255 255 255 255 255 255 255 255 255 255 255 255
##  [523] 255 255 255 255 255 255 255 255 255 255 255 255 255 255 255 255 255 255
##  [541] 255 255 255 255 255 255 255 255 255 255 255 255 255 255 255 255 255 255
##  [559] 255 255 255 255 255 255 255 255 255 255 255 255 255 255 255 255 255 255
##  [577] 255 255 255 255 255 255 255 255 255 255 255 255 255 255 255 255 255 255
##  [595] 255 255 255 255 255 255 255 255 255 255 255 255 255 255 255 255 255 255
##  [613] 255 255 255 255 255 255 255 255 255 255 255 255 255 255 255 255 255 255
##  [631] 255 255 255 255 255 255 255 255 255 255 255 255 255 255 255 255 255 255
##  [649] 255 255 255 255 255 255 255 255 255 255 255 255 255 255 255 255 255 255
##  [667] 255 255 255 255 255 255 255 255 255 255 255 255 255 255 255 255 255 255
##  [685] 255 255 255 255 255 255 255 255 255 255 255 255 255 255 255 255 255 255
##  [703] 255 255 255 255 255 255 255 255 255 255 255 255 255 255 255 255 255 255
##  [721] 255 255 255 255 255 255 255 255 255 255 255 255 255 255 255 255 255 255
##  [739] 255 255 255 255 255 255 255 255 255 255 255 255 255 255 255 255 255 255
##  [757] 255 255 255 255 255 255 255 255 255 255 255 255 255 255 255 255 255 255
##  [775] 255 255 255 255 255 255 255 255 255 255 255 255 255 255 255 255 255 255
##  [793] 255 255 255 255 255 255 255 255 255 255 255 255 255 255 255 255 255 255
##  [811] 255 255 255 255 255 255 255 255 255 255 255 255 255 255 255 255 255 255
##  [829] 255 255 255 255 255 255 255 255 255 255 255 255 255 255 255 255 255 255
##  [847] 255 255 255 255 255 255 255 255 255 255 255 255 255 255 255 255 255 255
##  [865] 255 255 255 255 255 255 255 255 255 255 255 255 255 255 255 255 255 255
##  [883] 255 255 255 255 255 255 255 255 255 255 255 255 255 255 255 255 255 255
##  [901] 255 255 255 255 255 255 255 255 255 255 255 255 255 255 255 255 255 255
##  [919] 255 255 255 255 255 255 255 255 255 255 255 255 255 255 255 255 255 255
##  [937] 255 255 255 255 255 255 255 255 255 255 255 255 255 255 255 255 255 255
##  [955] 255 255 255 255 255 255 255 255 255 255 255 255 255 255 255 255 255 255
##  [973] 255 255 255 255 255 255 255 255 255 255 255 255 255 255 255 255 255 255
##  [991] 255 255 255 255 255 255 255 255 255 255 255 255 255 255 255 255 255 255
## [1009] 255 255 255 255 255 255 255 255 255 255 255 255 255 255 255 255 255 255
## [1027] 255 255 255 255 255 255 255 255 255 255 255 255 255 255 255 255 255 255
## [1045] 255
head(cummax(d2$Total))
## [1] 318 405 525 625 625 625
head(cummax(d2$Attack))
## [1]  49  62  82 100 100 100
head(cummax(d2$Defence))
## [1]  49  63  83 123 123 123
head(cummax(d2$Sp_attack))
## [1]  65  80 100 122 122 122
head(cummax(d2$Sp_defence))
## [1]  65  80 100 120 120 120
head(cummax(d2$Speed))
## [1] 45 60 80 80 80 80
cummin(d2$HP)
##    [1] 45 45 45 45 39 39 39 39 39 39 39 39 39 39 39 39 39 39 39 39 39 39 39 39
##   [25] 30 30 30 30 30 30 30 30 30 30 30 30 30 30 30 30 30 30 30 30 30 30 30 30
##   [49] 30 30 30 30 30 30 30 30 30 30 30 30 30 30 30 10 10 10 10 10 10 10 10 10
##   [73] 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10
##   [97] 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10
##  [121] 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10
##  [145] 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10
##  [169] 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10
##  [193] 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10
##  [217] 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10
##  [241] 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10
##  [265] 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10
##  [289] 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10
##  [313] 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10
##  [337] 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10  1  1  1  1  1  1  1  1  1
##  [361]  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1
##  [385]  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1
##  [409]  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1
##  [433]  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1
##  [457]  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1
##  [481]  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1
##  [505]  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1
##  [529]  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1
##  [553]  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1
##  [577]  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1
##  [601]  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1
##  [625]  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1
##  [649]  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1
##  [673]  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1
##  [697]  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1
##  [721]  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1
##  [745]  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1
##  [769]  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1
##  [793]  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1
##  [817]  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1
##  [841]  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1
##  [865]  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1
##  [889]  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1
##  [913]  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1
##  [937]  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1
##  [961]  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1
##  [985]  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1
## [1009]  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1
## [1033]  1  1  1  1  1  1  1  1  1  1  1  1  1
cummin(d2$Attack)
##    [1] 49 49 49 49 49 49 49 49 49 48 48 48 48 30 20 20 20 20 20 20 20 20 20 20
##   [25] 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20
##   [49] 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20
##   [73] 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20
##   [97] 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20
##  [121] 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20
##  [145] 20 20 20  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5
##  [169]  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5
##  [193]  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5
##  [217]  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5
##  [241]  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5
##  [265]  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5
##  [289]  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5
##  [313]  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5
##  [337]  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5
##  [361]  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5
##  [385]  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5
##  [409]  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5
##  [433]  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5
##  [457]  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5
##  [481]  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5
##  [505]  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5
##  [529]  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5
##  [553]  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5
##  [577]  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5
##  [601]  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5
##  [625]  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5
##  [649]  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5
##  [673]  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5
##  [697]  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5
##  [721]  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5
##  [745]  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5
##  [769]  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5
##  [793]  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5
##  [817]  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5
##  [841]  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5
##  [865]  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5
##  [889]  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5
##  [913]  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5
##  [937]  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5
##  [961]  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5
##  [985]  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5
## [1009]  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5
## [1033]  5  5  5  5  5  5  5  5  5  5  5  5  5
cummin(d2$Defence)
##    [1] 49 49 49 49 43 43 43 43 43 43 43 43 43 35 35 35 30 30 30 30 30 30 30 30
##   [25] 30 30 30 30 30 30 30 30 30 30 30 30 30 30 30 30 30 30 30 30 30 30 30 30
##   [49] 30 30 30 30 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20
##   [73] 20 20 20 20 20 20 20 20 20 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15
##   [97] 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15
##  [121] 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15
##  [145] 15 15 15  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5
##  [169]  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5
##  [193]  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5
##  [217]  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5
##  [241]  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5
##  [265]  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5
##  [289]  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5
##  [313]  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5
##  [337]  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5
##  [361]  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5
##  [385]  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5
##  [409]  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5
##  [433]  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5
##  [457]  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5
##  [481]  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5
##  [505]  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5
##  [529]  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5
##  [553]  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5
##  [577]  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5
##  [601]  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5
##  [625]  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5
##  [649]  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5
##  [673]  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5
##  [697]  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5
##  [721]  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5
##  [745]  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5
##  [769]  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5
##  [793]  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5
##  [817]  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5
##  [841]  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5
##  [865]  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5
##  [889]  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5
##  [913]  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5
##  [937]  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5
##  [961]  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5
##  [985]  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5
## [1009]  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5
## [1033]  5  5  5  5  5  5  5  5  5  5  5  5  5
cummin(d2$Sp_attack)
##    [1] 65 65 65 65 60 60 60 60 60 50 50 50 50 20 20 20 20 20 20 15 15 15 15 15
##   [25] 15 15 15 15 15 15 15 15 15 15 15 15 15 10 10 10 10 10 10 10 10 10 10 10
##   [49] 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10
##   [73] 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10
##   [97] 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10
##  [121] 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10
##  [145] 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10
##  [169] 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10
##  [193] 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10
##  [217] 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10
##  [241] 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10
##  [265] 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10
##  [289] 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10
##  [313] 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10
##  [337] 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10
##  [361] 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10
##  [385] 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10
##  [409] 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10
##  [433] 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10
##  [457] 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10
##  [481] 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10
##  [505] 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10
##  [529] 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10
##  [553] 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10
##  [577] 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10
##  [601] 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10
##  [625] 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10
##  [649] 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10
##  [673] 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10
##  [697] 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10
##  [721] 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10
##  [745] 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10
##  [769] 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10
##  [793] 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10
##  [817] 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10
##  [841] 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10
##  [865] 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10
##  [889] 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10
##  [913] 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10
##  [937] 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10
##  [961] 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10
##  [985] 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10
## [1009] 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10
## [1033] 10 10 10 10 10 10 10 10 10 10 10 10 10
cumprod(d2$HP)
##    [1]  4.500000e+01  2.700000e+03  2.160000e+05  1.728000e+07  6.739200e+08
##    [6]  3.908736e+10  3.048814e+12  2.378075e+14  1.854898e+16  8.161553e+17
##   [11]  4.815316e+19  3.804100e+21  3.005239e+23  1.352358e+25  6.761788e+26
##   [16]  4.057073e+28  1.622829e+30  7.302731e+31  4.746775e+33  3.085404e+35
##   [21]  1.234162e+37  7.775217e+38  6.453431e+40  5.356347e+42  1.606904e+44
##   [26]  4.820713e+45  2.651392e+47  1.988544e+49  7.954176e+50  5.170214e+52
##   [31]  1.809575e+54  1.085745e+56  3.800107e+57  1.710048e+59  1.026029e+61
##   [36]  6.156174e+62  3.078087e+64  1.539044e+66  1.154283e+68  8.657120e+69
##   [41]  4.761416e+71  3.332991e+73  2.999692e+75  1.379858e+77  8.417136e+78
##   [46]  6.817880e+80  4.772516e+82  4.533890e+84  1.722878e+86  6.546937e+87
##   [51]  4.779264e+89  3.488863e+91  4.012192e+93  5.617069e+95  2.246828e+97
##   [56]  1.685121e+99 7.583044e+100 4.549826e+102 3.412370e+104 1.194329e+106
##   [61] 7.165976e+107 4.299586e+109 3.009710e+111 3.009710e+112 3.009710e+113
##   [66] 1.053399e+115 3.686895e+116 1.474758e+118 5.899032e+119 2.949516e+121
##   [71] 1.917185e+123 1.246170e+125 6.230852e+126 4.984682e+128 1.993873e+130
##   [76] 1.296017e+132 7.128095e+133 6.415285e+135 2.566114e+137 1.667974e+139
##   [81] 1.501177e+141 3.752942e+142 1.501177e+144 8.256472e+145 4.541060e+147
##   [86] 3.178742e+149 2.542993e+151 2.288694e+153 1.144347e+155 7.438256e+156
##   [91] 5.950605e+158 2.380242e+160 1.904194e+162 7.616774e+163 3.046710e+165
##   [96] 1.675690e+167 9.216297e+168 7.373037e+170 5.898430e+172 2.949215e+174
##  [101] 1.474607e+176 9.584948e+177 6.230217e+179 5.607195e+181 5.046475e+183
##  [106] 4.794152e+185 4.554444e+187 4.326722e+189 1.081680e+191 5.408402e+192
##  [111] 2.812369e+194 1.462432e+196 5.118512e+197 3.071107e+199 1.996220e+201
##  [116] 1.796598e+203 1.437278e+205 1.149823e+207 1.207314e+209 1.267679e+211
##  [121] 3.803038e+212 1.901519e+214 5.704557e+215 2.567051e+217 1.540230e+219
##  [126] 9.241382e+220 3.234484e+222 1.940690e+224 1.649587e+226 4.948760e+227
##  [131] 2.721818e+229 1.088727e+231 6.532363e+232 3.919418e+234 3.723447e+236
##  [136] 3.537275e+238 1.768637e+240 1.061182e+242 6.367095e+243 3.183547e+245
##  [141] 1.591774e+247 1.432596e+249 5.730385e+250 3.724750e+252 2.421088e+254
##  [146] 1.936870e+256 2.033714e+258 5.084284e+260 3.304785e+262 3.470024e+264
##  [151] 3.643525e+266 1.093058e+268 6.011817e+269 2.705317e+271 2.164254e+273
##  [156] 6.492762e+274 3.895657e+276 1.558263e+278 7.791314e+279 5.453920e+281
##  [161] 3.545048e+283 2.304281e+285 1.497783e+287 9.735588e+288 6.328132e+290
##  [166] 4.746099e+292 9.492198e+293 9.017589e+295 8.566709e+297 1.113672e+300
##  [171] 5.345626e+301 2.940095e+303 1.911061e+305 2.484380e+307           Inf
##  [176]           Inf           Inf           Inf           Inf           Inf
##  [181]           Inf           Inf           Inf           Inf           Inf
##  [186]           Inf           Inf           Inf           Inf           Inf
##  [191]           Inf           Inf           Inf           Inf           Inf
##  [196]           Inf           Inf           Inf           Inf           Inf
##  [201]           Inf           Inf           Inf           Inf           Inf
##  [206]           Inf           Inf           Inf           Inf           Inf
##  [211]           Inf           Inf           Inf           Inf           Inf
##  [216]           Inf           Inf           Inf           Inf           Inf
##  [221]           Inf           Inf           Inf           Inf           Inf
##  [226]           Inf           Inf           Inf           Inf           Inf
##  [231]           Inf           Inf           Inf           Inf           Inf
##  [236]           Inf           Inf           Inf           Inf           Inf
##  [241]           Inf           Inf           Inf           Inf           Inf
##  [246]           Inf           Inf           Inf           Inf           Inf
##  [251]           Inf           Inf           Inf           Inf           Inf
##  [256]           Inf           Inf           Inf           Inf           Inf
##  [261]           Inf           Inf           Inf           Inf           Inf
##  [266]           Inf           Inf           Inf           Inf           Inf
##  [271]           Inf           Inf           Inf           Inf           Inf
##  [276]           Inf           Inf           Inf           Inf           Inf
##  [281]           Inf           Inf           Inf           Inf           Inf
##  [286]           Inf           Inf           Inf           Inf           Inf
##  [291]           Inf           Inf           Inf           Inf           Inf
##  [296]           Inf           Inf           Inf           Inf           Inf
##  [301]           Inf           Inf           Inf           Inf           Inf
##  [306]           Inf           Inf           Inf           Inf           Inf
##  [311]           Inf           Inf           Inf           Inf           Inf
##  [316]           Inf           Inf           Inf           Inf           Inf
##  [321]           Inf           Inf           Inf           Inf           Inf
##  [326]           Inf           Inf           Inf           Inf           Inf
##  [331]           Inf           Inf           Inf           Inf           Inf
##  [336]           Inf           Inf           Inf           Inf           Inf
##  [341]           Inf           Inf           Inf           Inf           Inf
##  [346]           Inf           Inf           Inf           Inf           Inf
##  [351]           Inf           Inf           Inf           Inf           Inf
##  [356]           Inf           Inf           Inf           Inf           Inf
##  [361]           Inf           Inf           Inf           Inf           Inf
##  [366]           Inf           Inf           Inf           Inf           Inf
##  [371]           Inf           Inf           Inf           Inf           Inf
##  [376]           Inf           Inf           Inf           Inf           Inf
##  [381]           Inf           Inf           Inf           Inf           Inf
##  [386]           Inf           Inf           Inf           Inf           Inf
##  [391]           Inf           Inf           Inf           Inf           Inf
##  [396]           Inf           Inf           Inf           Inf           Inf
##  [401]           Inf           Inf           Inf           Inf           Inf
##  [406]           Inf           Inf           Inf           Inf           Inf
##  [411]           Inf           Inf           Inf           Inf           Inf
##  [416]           Inf           Inf           Inf           Inf           Inf
##  [421]           Inf           Inf           Inf           Inf           Inf
##  [426]           Inf           Inf           Inf           Inf           Inf
##  [431]           Inf           Inf           Inf           Inf           Inf
##  [436]           Inf           Inf           Inf           Inf           Inf
##  [441]           Inf           Inf           Inf           Inf           Inf
##  [446]           Inf           Inf           Inf           Inf           Inf
##  [451]           Inf           Inf           Inf           Inf           Inf
##  [456]           Inf           Inf           Inf           Inf           Inf
##  [461]           Inf           Inf           Inf           Inf           Inf
##  [466]           Inf           Inf           Inf           Inf           Inf
##  [471]           Inf           Inf           Inf           Inf           Inf
##  [476]           Inf           Inf           Inf           Inf           Inf
##  [481]           Inf           Inf           Inf           Inf           Inf
##  [486]           Inf           Inf           Inf           Inf           Inf
##  [491]           Inf           Inf           Inf           Inf           Inf
##  [496]           Inf           Inf           Inf           Inf           Inf
##  [501]           Inf           Inf           Inf           Inf           Inf
##  [506]           Inf           Inf           Inf           Inf           Inf
##  [511]           Inf           Inf           Inf           Inf           Inf
##  [516]           Inf           Inf           Inf           Inf           Inf
##  [521]           Inf           Inf           Inf           Inf           Inf
##  [526]           Inf           Inf           Inf           Inf           Inf
##  [531]           Inf           Inf           Inf           Inf           Inf
##  [536]           Inf           Inf           Inf           Inf           Inf
##  [541]           Inf           Inf           Inf           Inf           Inf
##  [546]           Inf           Inf           Inf           Inf           Inf
##  [551]           Inf           Inf           Inf           Inf           Inf
##  [556]           Inf           Inf           Inf           Inf           Inf
##  [561]           Inf           Inf           Inf           Inf           Inf
##  [566]           Inf           Inf           Inf           Inf           Inf
##  [571]           Inf           Inf           Inf           Inf           Inf
##  [576]           Inf           Inf           Inf           Inf           Inf
##  [581]           Inf           Inf           Inf           Inf           Inf
##  [586]           Inf           Inf           Inf           Inf           Inf
##  [591]           Inf           Inf           Inf           Inf           Inf
##  [596]           Inf           Inf           Inf           Inf           Inf
##  [601]           Inf           Inf           Inf           Inf           Inf
##  [606]           Inf           Inf           Inf           Inf           Inf
##  [611]           Inf           Inf           Inf           Inf           Inf
##  [616]           Inf           Inf           Inf           Inf           Inf
##  [621]           Inf           Inf           Inf           Inf           Inf
##  [626]           Inf           Inf           Inf           Inf           Inf
##  [631]           Inf           Inf           Inf           Inf           Inf
##  [636]           Inf           Inf           Inf           Inf           Inf
##  [641]           Inf           Inf           Inf           Inf           Inf
##  [646]           Inf           Inf           Inf           Inf           Inf
##  [651]           Inf           Inf           Inf           Inf           Inf
##  [656]           Inf           Inf           Inf           Inf           Inf
##  [661]           Inf           Inf           Inf           Inf           Inf
##  [666]           Inf           Inf           Inf           Inf           Inf
##  [671]           Inf           Inf           Inf           Inf           Inf
##  [676]           Inf           Inf           Inf           Inf           Inf
##  [681]           Inf           Inf           Inf           Inf           Inf
##  [686]           Inf           Inf           Inf           Inf           Inf
##  [691]           Inf           Inf           Inf           Inf           Inf
##  [696]           Inf           Inf           Inf           Inf           Inf
##  [701]           Inf           Inf           Inf           Inf           Inf
##  [706]           Inf           Inf           Inf           Inf           Inf
##  [711]           Inf           Inf           Inf           Inf           Inf
##  [716]           Inf           Inf           Inf           Inf           Inf
##  [721]           Inf           Inf           Inf           Inf           Inf
##  [726]           Inf           Inf           Inf           Inf           Inf
##  [731]           Inf           Inf           Inf           Inf           Inf
##  [736]           Inf           Inf           Inf           Inf           Inf
##  [741]           Inf           Inf           Inf           Inf           Inf
##  [746]           Inf           Inf           Inf           Inf           Inf
##  [751]           Inf           Inf           Inf           Inf           Inf
##  [756]           Inf           Inf           Inf           Inf           Inf
##  [761]           Inf           Inf           Inf           Inf           Inf
##  [766]           Inf           Inf           Inf           Inf           Inf
##  [771]           Inf           Inf           Inf           Inf           Inf
##  [776]           Inf           Inf           Inf           Inf           Inf
##  [781]           Inf           Inf           Inf           Inf           Inf
##  [786]           Inf           Inf           Inf           Inf           Inf
##  [791]           Inf           Inf           Inf           Inf           Inf
##  [796]           Inf           Inf           Inf           Inf           Inf
##  [801]           Inf           Inf           Inf           Inf           Inf
##  [806]           Inf           Inf           Inf           Inf           Inf
##  [811]           Inf           Inf           Inf           Inf           Inf
##  [816]           Inf           Inf           Inf           Inf           Inf
##  [821]           Inf           Inf           Inf           Inf           Inf
##  [826]           Inf           Inf           Inf           Inf           Inf
##  [831]           Inf           Inf           Inf           Inf           Inf
##  [836]           Inf           Inf           Inf           Inf           Inf
##  [841]           Inf           Inf           Inf           Inf           Inf
##  [846]           Inf           Inf           Inf           Inf           Inf
##  [851]           Inf           Inf           Inf           Inf           Inf
##  [856]           Inf           Inf           Inf           Inf           Inf
##  [861]           Inf           Inf           Inf           Inf           Inf
##  [866]           Inf           Inf           Inf           Inf           Inf
##  [871]           Inf           Inf           Inf           Inf           Inf
##  [876]           Inf           Inf           Inf           Inf           Inf
##  [881]           Inf           Inf           Inf           Inf           Inf
##  [886]           Inf           Inf           Inf           Inf           Inf
##  [891]           Inf           Inf           Inf           Inf           Inf
##  [896]           Inf           Inf           Inf           Inf           Inf
##  [901]           Inf           Inf           Inf           Inf           Inf
##  [906]           Inf           Inf           Inf           Inf           Inf
##  [911]           Inf           Inf           Inf           Inf           Inf
##  [916]           Inf           Inf           Inf           Inf           Inf
##  [921]           Inf           Inf           Inf           Inf           Inf
##  [926]           Inf           Inf           Inf           Inf           Inf
##  [931]           Inf           Inf           Inf           Inf           Inf
##  [936]           Inf           Inf           Inf           Inf           Inf
##  [941]           Inf           Inf           Inf           Inf           Inf
##  [946]           Inf           Inf           Inf           Inf           Inf
##  [951]           Inf           Inf           Inf           Inf           Inf
##  [956]           Inf           Inf           Inf           Inf           Inf
##  [961]           Inf           Inf           Inf           Inf           Inf
##  [966]           Inf           Inf           Inf           Inf           Inf
##  [971]           Inf           Inf           Inf           Inf           Inf
##  [976]           Inf           Inf           Inf           Inf           Inf
##  [981]           Inf           Inf           Inf           Inf           Inf
##  [986]           Inf           Inf           Inf           Inf           Inf
##  [991]           Inf           Inf           Inf           Inf           Inf
##  [996]           Inf           Inf           Inf           Inf           Inf
## [1001]           Inf           Inf           Inf           Inf           Inf
## [1006]           Inf           Inf           Inf           Inf           Inf
## [1011]           Inf           Inf           Inf           Inf           Inf
## [1016]           Inf           Inf           Inf           Inf           Inf
## [1021]           Inf           Inf           Inf           Inf           Inf
## [1026]           Inf           Inf           Inf           Inf           Inf
## [1031]           Inf           Inf           Inf           Inf           Inf
## [1036]           Inf           Inf           Inf           Inf           Inf
## [1041]           Inf           Inf           Inf           Inf           Inf
seq_along(d2)
## [1] 1 2 3 4 5 6 7 8
seq(from = 1, to = 5, by =2)
## [1] 1 3 5
cumsum(d2$HP) / seq_along(d2) 
## Warning in cumsum(d2$HP)/seq_along(d2): longer object length is not a multiple
## of shorter object length
##    [1]    45.00000    52.50000    61.66667    66.25000    60.80000    60.33333
##    [7]    62.85714    64.75000   596.00000   320.00000   233.00000   194.50000
##   [13]   171.40000   150.33333   136.00000   126.50000  1052.00000   548.50000
##   [19]   387.33333   306.75000   253.40000   221.66667   201.85714   187.00000
##   [25]  1526.00000   778.00000   537.00000   421.50000   345.20000   298.50000
##   [31]   260.85714   235.75000  1921.00000   983.00000   675.33333   521.50000
##   [37]   427.20000   364.33333   323.00000   292.00000  2391.00000  1230.50000
##   [43]   850.33333   649.25000   531.60000   456.50000   401.28571   363.00000
##   [49]  2942.00000  1490.00000  1017.66667   781.50000   648.20000   563.50000
##   [55]   488.71429   437.00000  3541.00000  1800.50000  1225.33333   927.75000
##   [61]   754.20000   638.50000   557.28571   488.87500  3921.00000  1978.00000
##   [67]  1330.33333  1007.75000   814.20000   686.83333   598.00000   531.37500
##   [73]  4301.00000  2190.50000  1473.66667  1121.50000   908.20000   771.83333
##   [79]   667.28571   592.00000  4826.00000  2425.50000  1630.33333  1236.50000
##   [85]  1000.20000   845.16667   735.85714   655.12500  5291.00000  2678.00000
##   [91]  1812.00000  1369.00000  1111.20000   932.66667   805.14286   711.37500
##   [97]  5746.00000  2913.00000  1968.66667  1489.00000  1201.20000  1011.83333
##  [103]   876.57143   778.25000  6316.00000  3205.50000  2168.66667  1650.25000
##  [109]  1325.20000  1112.66667   961.14286   847.50000  6815.00000  3437.50000
##  [115]  2313.33333  1757.50000  1422.00000  1198.33333  1042.14286   925.00000
##  [121]  7430.00000  3740.00000  2503.33333  1888.75000  1523.00000  1279.16667
##  [127]  1101.42857   971.25000  7855.00000  3942.50000  2646.66667  1995.00000
##  [133]  1608.00000  1350.00000  1170.71429  1036.25000  8340.00000  4200.00000
##  [139]  2820.00000  2127.50000  1712.00000  1441.66667  1241.42857  1094.37500
##  [145]  8820.00000  4450.00000  3001.66667  2313.75000  1864.00000  1570.83333
##  [151]  1361.42857  1195.00000  9615.00000  4830.00000  3246.66667  2442.50000
##  [157]  1966.00000  1645.00000  1417.14286  1248.75000 10055.00000  5060.00000
##  [163]  3395.00000  2562.50000  2063.00000  1731.66667  1487.14286  1313.12500
##  [169] 10600.00000  5365.00000  3592.66667  2708.25000  2179.60000  1838.00000
##  [175]  1584.71429  1394.75000 11223.00000  5629.00000  3776.00000  2839.50000
##  [181]  2283.60000  1916.33333  1654.00000  1467.25000 11828.00000  5959.00000
##  [187]  4002.66667  3024.50000  2437.60000  2046.33333  1759.85714  1547.50000
##  [193] 12471.00000  6288.50000  4227.66667  3197.25000  2577.80000  2155.66667
##  [199]  1856.28571  1634.25000 13113.00000  6585.50000  4416.33333  3324.75000
##  [205]  2672.80000  2241.50000  1926.28571  1696.12500 13629.00000  6864.50000
##  [211]  4589.66667  3456.00000  2772.80000  2322.33333  2002.71429  1761.75000
##  [217] 14219.00000  7119.50000  4763.00000  3594.75000  2882.80000  2411.50000
##  [223]  2072.71429  1821.75000 14629.00000  7349.50000  4929.66667  3719.75000
##  [229]  2990.80000  2504.00000  2160.57143  1899.25000 15284.00000  7659.50000
##  [235]  5124.66667  3862.25000  3100.80000  2589.00000  2229.85714  1959.25000
##  [241] 15729.00000  7912.00000  5296.33333  3996.00000  3208.80000  2689.83333
##  [247]  2319.14286  2036.75000 16342.00000  8266.00000  5534.00000  4163.00000
##  [253]  3345.40000  2804.50000  2413.14286  2120.87500 17042.00000  8551.00000
##  [259]  5730.66667  4314.25000  3465.40000  2899.50000  2488.14286  2187.12500
##  [265] 17577.00000  8816.00000  5897.33333  4445.50000  3564.40000  2980.33333
##  [271]  2561.71429  2254.00000 18097.00000  9078.50000  6064.00000  4566.75000
##  [277]  3662.40000  3066.16667  2637.42857  2313.37500 18582.00000  9328.50000
##  [283]  6244.00000  4705.50000  3782.40000  3166.16667  2724.28571  2390.62500
##  [289] 19160.00000  9605.00000  6418.33333  4825.00000  3869.00000  3240.00000
##  [295]  2813.57143  2473.12500 19900.00000 10000.00000  6683.33333  5030.00000
##  [301]  4044.00000  3386.66667  2918.00000  2566.50000 20632.00000 10336.00000
##  [307]  6907.33333  5198.00000  4172.40000  3484.50000  2995.28571  2630.87500
##  [313] 21127.00000 10588.50000  7082.33333  5336.75000  4289.40000  3580.33333
##  [319]  3078.85714  2698.75000 21628.00000 10853.00000  7261.33333  5457.25000
##  [325]  4375.80000  3656.50000  3141.28571  2756.12500 22089.00000 11074.50000
##  [331]  7409.66667  5567.25000  4467.80000  3738.16667  3209.85714  2816.12500
##  [337] 22569.00000 11314.50000  7552.33333  5673.75000  4552.60000  3805.16667
##  [343]  3267.28571  2867.62500 23001.00000 11530.50000  7707.00000  5800.25000
##  [349]  4670.20000  3897.00000  3349.00000  2930.50000 23508.00000 11796.00000
##  [355]  7898.66667  5942.00000  4782.40000  3993.66667  3427.42857  3005.25000
##  [361] 24112.00000 12081.00000  8070.66667  6065.50000  4862.40000  4060.33333
##  [367]  3488.85714  3061.50000 24562.00000 12296.00000  8217.33333  6178.00000
##  [373]  4950.40000  4137.00000  3556.00000  3119.00000 25012.00000 12538.50000
##  [379]  8380.66667  6298.00000  5052.40000  4227.00000  3629.57143  3184.62500
##  [385] 25547.00000 12838.50000  8615.66667  6476.75000  5195.40000  4341.16667
##  [391]  3731.00000  3272.12500 26257.00000 13158.50000  8787.33333  6603.00000
##  [397]  5298.40000  4423.66667  3801.71429  3332.12500 26732.00000 13403.50000
##  [403]  8960.00000  6738.25000  5408.60000  4522.16667  3883.28571  3411.62500
##  [409] 27336.00000 13699.50000  9146.33333  6874.75000  5513.00000  4608.50000
##  [415]  3956.57143  3471.37500 27791.00000 13943.00000  9318.66667  7006.50000
##  [421]  5619.20000  4694.33333  4032.28571  3533.75000 28334.00000 14199.00000
##  [427]  9472.66667  7114.50000  5711.40000  4772.00000  4099.57143  3595.25000
##  [433] 28857.00000 14453.50000  9662.33333  7266.75000  5827.40000  4871.16667
##  [439]  4191.00000  3671.50000 29427.00000 14741.00000  9860.66667  7406.25000
##  [445]  5934.00000  4955.83333  4261.42857  3740.62500 29965.00000 15012.50000
##  [451] 10035.00000  7546.25000  6053.00000  5057.50000  4346.42857  3813.12500
##  [457] 30585.00000 15332.50000 10248.33333  7711.25000  6189.00000  5174.16667
##  [463]  4449.28571  3906.25000 31355.00000 15727.50000 10501.66667  7888.75000
##  [469]  6321.00000  5275.83333  4530.00000  3973.12500 31880.00000 15962.00000
##  [475] 10662.66667  8016.00000  6423.40000  5363.50000  4609.28571  4038.12500
##  [481] 32360.00000 16222.50000 10834.66667  8145.75000  6524.00000  5449.50000
##  [487]  4677.42857  4100.25000 32882.00000 16461.00000 10994.00000  8262.25000
##  [493]  6629.20000  5529.33333  4748.00000  4159.50000 33336.00000 16698.00000
##  [499] 11152.00000  8381.50000  6711.20000  5604.33333  4812.28571  4217.62500
##  [505] 33826.00000 16935.50000 11313.66667  8504.25000  6825.60000  5700.50000
##  [511]  4899.00000  4305.37500 34498.00000 17281.50000 11542.66667  8672.00000
##  [517]  6957.60000  5806.16667  4986.85714  4369.12500 35016.00000 17559.50000
##  [523] 11725.33333  8810.75000  7058.60000  5885.50000  5059.00000  4436.12500
##  [529] 35539.00000 17798.50000 11888.33333  8943.25000  7176.20000  6002.66667
##  [535]  5150.85714  4515.75000 36196.00000 18132.00000 12124.00000  9103.00000
##  [541]  7296.40000  6088.33333  5230.42857  4585.87500 36736.00000 18402.50000
##  [547] 12283.33333  9227.50000  7400.00000  6181.66667  5308.57143  4653.75000
##  [553] 37340.00000 18727.50000 12518.33333  9407.50000  7541.00000  6298.33333
##  [559]  5410.85714  4742.62500 38006.00000 19040.50000 12730.33333  9569.00000
##  [565]  7668.80000  6402.00000  5496.00000  4814.62500 38587.00000 19318.50000
##  [571] 12895.66667  9684.25000  7757.40000  6472.83333  5555.28571  4870.25000
##  [577] 39042.00000 19558.50000 13072.33333  9826.75000  7879.60000  6584.66667
##  [583]  5665.42857  4976.00000 39928.00000 20004.00000 13369.33333 10044.50000
##  [589]  8055.60000  6729.66667  5785.42857  5074.75000 40643.00000 20351.50000
##  [595] 13592.66667 10210.75000  8186.60000  6840.50000  5871.14286  5146.62500
##  [601] 41268.00000 20656.50000 13791.00000 10354.50000  8296.60000  6928.00000
##  [607]  5944.14286  5209.12500 41723.00000 20899.00000 13949.33333 10480.75000
##  [613]  8394.60000  7008.00000  6017.71429  5280.00000 42290.00000 21176.00000
##  [619] 14144.00000 10619.25000  8510.40000  7101.16667  6096.71429  5345.25000
##  [625] 42827.00000 21447.00000 14318.00000 10766.00000  8633.40000  7211.66667
##  [631]  6192.14286  5428.75000 43535.00000 21792.50000 14553.33333 10941.25000
##  [637]  8777.00000  7326.66667  6286.42857  5507.50000 44135.00000 22082.50000
##  [643] 14735.00000 11066.25000  8861.00000  7394.16667  6344.28571  5560.00000
##  [649] 44550.00000 22310.00000 14890.00000 11182.50000  8965.00000  7482.50000
##  [655]  6423.57143  5633.75000 45175.00000 22640.00000 15128.33333 11365.00000
##  [661]  9102.00000  7596.66667  6518.57143  5711.87500 45767.00000 22902.50000
##  [667] 15281.00000 11475.25000  9191.00000  7671.50000  6583.42857  5769.87500
##  [673] 46209.00000 23144.50000 15443.00000 11597.25000  9288.80000  7753.16667
##  [679]  6652.00000  5828.00000 46694.00000 23369.50000 15601.33333 11728.50000
##  [685]  9395.20000  7841.83333  6726.71429  5892.25000 47209.00000 23634.50000
##  [691] 15783.00000 11851.00000  9490.80000  7920.66667  6799.00000  5963.37500
##  [697] 47762.00000 23931.00000 16009.00000 12019.25000  9629.40000  8031.83333
##  [703]  6895.00000  6038.12500 48365.00000 24212.50000 16153.33333 12131.25000
##  [709]  9722.00000  8110.83333  6962.85714  6098.75000 48850.00000 24455.00000
##  [715] 16318.66667 12255.50000  9819.60000  8192.16667  7035.42857  6166.00000
##  [721] 49378.00000 24729.00000 16522.33333 12419.00000  9944.20000  8297.66667
##  [727]  7123.28571  6240.25000 50011.00000 25028.00000 16707.00000 12554.00000
##  [733] 10057.20000  8397.66667  7208.00000  6320.75000 50651.00000 25354.50000
##  [739] 16920.33333 12708.25000 10185.00000  8496.66667  7295.00000  6394.50000
##  [745] 51247.00000 25669.00000 17139.00000 12874.00000 10315.00000  8609.00000
##  [751]  7393.42857  6481.75000 51943.00000 26016.00000 17385.66667 13070.50000
##  [757] 10481.40000  8749.66667  7512.71429  6586.12500 52789.00000 26430.00000
##  [763] 17638.66667 13244.25000 10613.00000  8850.83333  7594.85714  6654.87500
##  [769] 53280.00000 26667.00000 17802.00000 13369.50000 10703.20000  8933.50000
##  [775]  7663.71429  6713.50000 53786.00000 26912.00000 17956.33333 13487.25000
##  [781] 10802.20000  9016.16667  7734.42857  6774.37500 54273.00000 27169.50000
##  [787] 18154.00000 13632.25000 10924.80000  9116.50000  7823.00000  6854.37500
##  [793] 54909.00000 27477.00000 18337.66667 13768.25000 11026.60000  9201.83333
##  [799]  7901.71429  6921.75000 55456.00000 27754.50000 18531.66667 13909.25000
##  [805] 11141.80000  9293.16667  7974.85714  6984.25000 55945.00000 27994.50000
##  [811] 18683.66667 14027.25000 11238.20000  9378.00000  8055.85714  7060.75000
##  [817] 56564.00000 28315.50000 18893.66667 14181.50000 11358.80000  9480.66667
##  [823]  8134.42857  7123.00000 57069.00000 28559.00000 19054.00000 14304.00000
##  [829] 11455.00000  9556.66667  8199.28571  7183.75000 57555.00000 28805.00000
##  [835] 19235.00000 14436.25000 11566.00000  9659.33333  8297.42857  7273.75000
##  [841] 58244.00000 29230.00000 19503.33333 14640.00000 11728.00000  9786.66667
##  [847]  8400.00000  7358.50000 58946.00000 29512.00000 19689.66667 14783.50000
##  [853] 11845.80000  9879.83333  8477.00000  7427.37500 59454.00000 29754.50000
##  [859] 19863.00000 14909.25000 11945.00000  9962.00000  8547.00000  7488.25000
##  [865] 59953.00000 30025.00000 20041.66667 15050.00000 12055.00000 10058.33333
##  [871]  8627.14286  7556.25000 60495.00000 30270.00000 20205.00000 15175.00000
##  [877] 12155.00000 10136.66667  8695.00000  7614.37500 60965.00000 30517.50000
##  [883] 20378.33333 15293.25000 12248.20000 10213.50000  8764.42857  7673.87500
##  [889] 61451.00000 30749.50000 20522.33333 15409.25000 12351.40000 10299.83333
##  [895]  8835.85714  7740.37500 61974.00000 31032.00000 20721.33333 15547.25000
##  [901] 12452.80000 10386.50000  8914.85714  7807.37500 62554.00000 31324.50000
##  [907] 20903.00000 15692.25000 12566.80000 10482.33333  8994.14286  7876.75000
##  [913] 63082.00000 31580.00000 21076.66667 15818.75000 12666.00000 10567.50000
##  [919]  9067.85714  7943.12500 63615.00000 31842.50000 21242.66667 15942.75000
##  [925] 12781.60000 10674.16667  9164.85714  8032.62500 64332.00000 32207.50000
##  [931] 21504.00000 16142.75000 12958.80000 10815.16667  9284.00000  8135.62500
##  [937] 65182.00000 32631.00000 21784.00000 16354.75000 13098.40000 10925.50000
##  [943]  9372.28571  8211.75000 65740.00000 32937.50000 21975.00000 16498.75000
##  [949] 13219.00000 11024.16667  9458.57143  8286.25000 66340.00000 33202.50000
##  [955] 22158.33333 16636.25000 13333.00000 11117.16667  9538.71429  8358.62500
##  [961] 66894.00000 33472.00000 22334.66667 16761.00000 13422.80000 11192.33333
##  [967]  9602.00000  8407.00000 67328.00000 33689.00000 22489.33333 16881.75000
##  [973] 13519.20000 11271.00000  9672.28571  8477.00000 67856.00000 33963.00000
##  [979] 22678.66667 17022.00000 13632.00000 11371.66667  9753.00000  8541.50000
##  [985] 68372.00000 34223.50000 22840.66667 17143.00000 13734.40000 11453.66667
##  [991]  9828.85714  8605.25000 68902.00000 34472.00000 23000.33333 17264.50000
##  [997] 13820.60000 11528.00000  9894.71429  8669.50000 69426.00000 34743.00000
## [1003] 23182.66667 17407.00000 13937.20000 11621.83333  9970.85714  8732.62500
## [1009] 69909.00000 34969.50000 23336.33333 17527.25000 14036.80000 11709.83333
## [1015] 10045.57143  8798.62500 70447.00000 35252.50000 23525.66667 17674.75000
## [1021] 14157.80000 11813.16667 10138.42857  8882.37500 71129.00000 35578.50000
## [1027] 23741.66667 17828.25000 14281.00000 11916.16667 10227.00000  8960.12500
## [1033] 71821.00000 36038.00000 24045.33333 18059.00000 14467.20000 12073.50000
## [1039] 10360.14286  9090.12500 72821.00000 36460.50000 24340.33333 18280.25000
## [1045] 14644.20000
md = seq_along(d2)
md
## [1] 1 2 3 4 5 6 7 8
mean(d2[,2])
## [1] 439.3148
mean(d2[2,])
## Warning in mean.default(d2[2, ]): argument is not numeric or logical: returning
## NA
## [1] NA
table(d2$HP)
## 
##   1  10  20  25  28  30  31  35  36  37  38  39  40  41  42  43  44  45  46  47 
##   1   2   6   4   2  16   1  17   1   1  11   2  48   4   4   5   7  47   3   2 
##  48  49  50  51  52  53  54  55  56  57  58  59  60  61  62  63  64  65  66  67 
##   6   3  76   2   5   3   5  41   1   5   9   8  83   6   8   3   6  58   3   6 
##  68  69  70  71  72  73  74  75  76  77  78  79  80  81  82  83  84  85  86  88 
##  13   3  77   5  10   6   5  58   5   4  13   7  54   1   2   4   2  22   4   4 
##  89  90  91  92  93  95  97  98  99 100 101 103 104 105 106 107 108 109 110 111 
##   3  40   7   5   1  29   7   1   1  44   1   3   1  14   5   1   4   3  11   1 
## 114 115 116 120 122 123 125 126 130 135 137 140 144 150 160 165 170 190 200 216 
##   1   3   1   5   1   2   4   2   3   2   2   2   1   4   1   1   1   1   1   1 
## 223 250 255 
##   1   1   2
d.mat = as.matrix(d2)
d.mat
##         Name                Total  HP    Attack Defence Sp_attack Sp_defence
##    [1,] "Bulbasaur"         " 318" " 45" " 49"  " 49"   " 65"     " 65"     
##    [2,] "Ivysaur"           " 405" " 60" " 62"  " 63"   " 80"     " 80"     
##    [3,] "Venusaur"          " 525" " 80" " 82"  " 83"   "100"     "100"     
##    [4,] "Mega Venusaur"     " 625" " 80" "100"  "123"   "122"     "120"     
##    [5,] "Charmander"        " 309" " 39" " 52"  " 43"   " 60"     " 50"     
##    [6,] "Charmeleon"        " 405" " 58" " 64"  " 58"   " 80"     " 65"     
##    [7,] "Charizard"         " 534" " 78" " 84"  " 78"   "109"     " 85"     
##    [8,] "Mega Charizard"    " 634" " 78" "130"  "111"   "130"     " 85"     
##    [9,] "Mega Charizard X"  " 634" " 78" "104"  " 78"   "159"     "115"     
##   [10,] "Squirtle"          " 314" " 44" " 48"  " 65"   " 50"     " 64"     
##   [11,] "Wartortle"         " 405" " 59" " 63"  " 80"   " 65"     " 80"     
##   [12,] "Blastoise"         " 530" " 79" " 83"  "100"   " 85"     "105"     
##   [13,] "Mega Blastoise"    " 630" " 79" "103"  "120"   "135"     "115"     
##   [14,] "Caterpie"          " 195" " 45" " 30"  " 35"   " 20"     " 20"     
##   [15,] "Metapod"           " 205" " 50" " 20"  " 55"   " 25"     " 25"     
##   [16,] "Butterfree"        " 395" " 60" " 45"  " 50"   " 90"     " 80"     
##   [17,] "Weedle"            " 195" " 40" " 35"  " 30"   " 20"     " 20"     
##   [18,] "Kakuna"            " 205" " 45" " 25"  " 50"   " 25"     " 25"     
##   [19,] "Beedrill"          " 395" " 65" " 90"  " 40"   " 45"     " 80"     
##   [20,] "Mega Beedrill"     " 495" " 65" "150"  " 40"   " 15"     " 80"     
##   [21,] "Pidgey"            " 251" " 40" " 45"  " 40"   " 35"     " 35"     
##   [22,] "Pidgeotto"         " 349" " 63" " 60"  " 55"   " 50"     " 50"     
##   [23,] "Pidgeot"           " 479" " 83" " 80"  " 75"   " 70"     " 70"     
##   [24,] "Mega Pidgeot"      " 579" " 83" " 80"  " 80"   "135"     " 80"     
##   [25,] "Rattata"           " 253" " 30" " 56"  " 35"   " 25"     " 35"     
##   [26,] "Mega Rattata"      " 253" " 30" " 56"  " 35"   " 25"     " 35"     
##   [27,] "Raticate"          " 413" " 55" " 81"  " 60"   " 50"     " 70"     
##   [28,] "Mega Raticate"     " 413" " 75" " 71"  " 70"   " 40"     " 80"     
##   [29,] "Spearow"           " 262" " 40" " 60"  " 30"   " 31"     " 31"     
##   [30,] "Fearow"            " 442" " 65" " 90"  " 65"   " 61"     " 61"     
##   [31,] "Ekans"             " 288" " 35" " 60"  " 44"   " 40"     " 54"     
##   [32,] "Arbok"             " 448" " 60" " 95"  " 69"   " 65"     " 79"     
##   [33,] "Pikachu"           " 320" " 35" " 55"  " 40"   " 50"     " 50"     
##   [34,] "Mega Pikachu"      " 430" " 45" " 80"  " 50"   " 75"     " 60"     
##   [35,] "Raichu"            " 485" " 60" " 90"  " 55"   " 90"     " 80"     
##   [36,] "Mega Raichu"       " 485" " 60" " 85"  " 50"   " 95"     " 85"     
##   [37,] "Sandshrew"         " 300" " 50" " 75"  " 85"   " 20"     " 30"     
##   [38,] "Mega Sandshrew"    " 300" " 50" " 75"  " 90"   " 10"     " 35"     
##   [39,] "Sandslash"         " 450" " 75" "100"  "110"   " 45"     " 55"     
##   [40,] "Mega Sandslash"    " 450" " 75" "100"  "120"   " 25"     " 65"     
##   [41,] "Nidoranâ\231\200"        " 275" " 55" " 47"  " 52"   " 40"     " 40"     
##   [42,] "Nidorina"          " 365" " 70" " 62"  " 67"   " 55"     " 55"     
##   [43,] "Nidoqueen"         " 505" " 90" " 92"  " 87"   " 75"     " 85"     
##   [44,] "Nidoranâ\231‚"        " 273" " 46" " 57"  " 40"   " 40"     " 40"     
##   [45,] "Nidorino"          " 365" " 61" " 72"  " 57"   " 55"     " 55"     
##   [46,] "Nidoking"          " 505" " 81" "102"  " 77"   " 85"     " 75"     
##   [47,] "Clefairy"          " 323" " 70" " 45"  " 48"   " 60"     " 65"     
##   [48,] "Clefable"          " 483" " 95" " 70"  " 73"   " 95"     " 90"     
##   [49,] "Vulpix"            " 299" " 38" " 41"  " 40"   " 50"     " 65"     
##   [50,] "Mega Vulpix"       " 299" " 38" " 41"  " 40"   " 50"     " 65"     
##   [51,] "Ninetales"         " 505" " 73" " 76"  " 75"   " 81"     "100"     
##   [52,] "Mega Ninetales"    " 505" " 73" " 67"  " 75"   " 81"     "100"     
##   [53,] "Jigglypuff"        " 270" "115" " 45"  " 20"   " 45"     " 25"     
##   [54,] "Wigglytuff"        " 435" "140" " 70"  " 45"   " 85"     " 50"     
##   [55,] "Zubat"             " 245" " 40" " 45"  " 35"   " 30"     " 40"     
##   [56,] "Golbat"            " 455" " 75" " 80"  " 70"   " 65"     " 75"     
##   [57,] "Oddish"            " 320" " 45" " 50"  " 55"   " 75"     " 65"     
##   [58,] "Gloom"             " 395" " 60" " 65"  " 70"   " 85"     " 75"     
##   [59,] "Vileplume"         " 490" " 75" " 80"  " 85"   "110"     " 90"     
##   [60,] "Paras"             " 285" " 35" " 70"  " 55"   " 45"     " 55"     
##   [61,] "Parasect"          " 405" " 60" " 95"  " 80"   " 60"     " 80"     
##   [62,] "Venonat"           " 305" " 60" " 55"  " 50"   " 40"     " 55"     
##   [63,] "Venomoth"          " 450" " 70" " 65"  " 60"   " 90"     " 75"     
##   [64,] "Diglett"           " 265" " 10" " 55"  " 25"   " 35"     " 45"     
##   [65,] "Mega Diglett"      " 265" " 10" " 55"  " 30"   " 35"     " 45"     
##   [66,] "Dugtrio"           " 425" " 35" "100"  " 50"   " 50"     " 70"     
##   [67,] "Mega Dugtrio"      " 425" " 35" "100"  " 60"   " 50"     " 70"     
##   [68,] "Meowth"            " 290" " 40" " 45"  " 35"   " 40"     " 40"     
##   [69,] "Mega Meowth"       " 290" " 40" " 35"  " 35"   " 50"     " 40"     
##   [70,] "Mega Meowth X"     " 290" " 50" " 65"  " 55"   " 40"     " 40"     
##   [71,] "Persian"           " 440" " 65" " 70"  " 60"   " 65"     " 65"     
##   [72,] "Mega Persian"      " 440" " 65" " 60"  " 60"   " 75"     " 65"     
##   [73,] "Psyduck"           " 320" " 50" " 52"  " 48"   " 65"     " 50"     
##   [74,] "Golduck"           " 500" " 80" " 82"  " 78"   " 95"     " 80"     
##   [75,] "Mankey"            " 305" " 40" " 80"  " 35"   " 35"     " 45"     
##   [76,] "Primeape"          " 455" " 65" "105"  " 60"   " 60"     " 70"     
##   [77,] "Growlithe"         " 350" " 55" " 70"  " 45"   " 70"     " 50"     
##   [78,] "Arcanine"          " 555" " 90" "110"  " 80"   "100"     " 80"     
##   [79,] "Poliwag"           " 300" " 40" " 50"  " 40"   " 40"     " 40"     
##   [80,] "Poliwhirl"         " 385" " 65" " 65"  " 65"   " 50"     " 50"     
##   [81,] "Poliwrath"         " 510" " 90" " 95"  " 95"   " 70"     " 90"     
##   [82,] "Abra"              " 310" " 25" " 20"  " 15"   "105"     " 55"     
##   [83,] "Kadabra"           " 400" " 40" " 35"  " 30"   "120"     " 70"     
##   [84,] "Alakazam"          " 500" " 55" " 50"  " 45"   "135"     " 95"     
##   [85,] "Mega Alakazam"     " 600" " 55" " 50"  " 65"   "175"     "105"     
##   [86,] "Machop"            " 305" " 70" " 80"  " 50"   " 35"     " 35"     
##   [87,] "Machoke"           " 405" " 80" "100"  " 70"   " 50"     " 60"     
##   [88,] "Machamp"           " 505" " 90" "130"  " 80"   " 65"     " 85"     
##   [89,] "Bellsprout"        " 300" " 50" " 75"  " 35"   " 70"     " 30"     
##   [90,] "Weepinbell"        " 390" " 65" " 90"  " 50"   " 85"     " 45"     
##   [91,] "Victreebel"        " 490" " 80" "105"  " 65"   "100"     " 70"     
##   [92,] "Tentacool"         " 335" " 40" " 40"  " 35"   " 50"     "100"     
##   [93,] "Tentacruel"        " 515" " 80" " 70"  " 65"   " 80"     "120"     
##   [94,] "Geodude"           " 300" " 40" " 80"  "100"   " 30"     " 30"     
##   [95,] "Mega Geodude"      " 300" " 40" " 80"  "100"   " 30"     " 30"     
##   [96,] "Graveler"          " 390" " 55" " 95"  "115"   " 45"     " 45"     
##   [97,] "Mega Graveler"     " 390" " 55" " 95"  "115"   " 45"     " 45"     
##   [98,] "Golem"             " 495" " 80" "120"  "130"   " 55"     " 65"     
##   [99,] "Mega Golem"        " 495" " 80" "120"  "130"   " 55"     " 65"     
##  [100,] "Ponyta"            " 410" " 50" " 85"  " 55"   " 65"     " 65"     
##  [101,] "Mega Ponyta"       " 410" " 50" " 85"  " 55"   " 65"     " 65"     
##  [102,] "Rapidash"          " 500" " 65" "100"  " 70"   " 80"     " 80"     
##  [103,] "Mega Rapidash"     " 500" " 65" "100"  " 70"   " 80"     " 80"     
##  [104,] "Slowpoke"          " 315" " 90" " 65"  " 65"   " 40"     " 40"     
##  [105,] "Mega Slowpoke"     " 315" " 90" " 65"  " 65"   " 40"     " 40"     
##  [106,] "Slowbro"           " 490" " 95" " 75"  "110"   "100"     " 80"     
##  [107,] "Mega Slowbro"      " 590" " 95" " 75"  "180"   "130"     " 80"     
##  [108,] "Mega Slowbro X"    " 490" " 95" "100"  " 95"   "100"     " 70"     
##  [109,] "Magnemite"         " 325" " 25" " 35"  " 70"   " 95"     " 55"     
##  [110,] "Magneton"          " 465" " 50" " 60"  " 95"   "120"     " 70"     
##  [111,] "Farfetch'd"        " 377" " 52" " 90"  " 55"   " 58"     " 62"     
##  [112,] "Mega Farfetch'd"   " 377" " 52" " 95"  " 55"   " 58"     " 62"     
##  [113,] "Doduo"             " 310" " 35" " 85"  " 45"   " 35"     " 35"     
##  [114,] "Dodrio"            " 470" " 60" "110"  " 70"   " 60"     " 60"     
##  [115,] "Seel"              " 325" " 65" " 45"  " 55"   " 45"     " 70"     
##  [116,] "Dewgong"           " 475" " 90" " 70"  " 80"   " 70"     " 95"     
##  [117,] "Grimer"            " 325" " 80" " 80"  " 50"   " 40"     " 50"     
##  [118,] "Mega Grimer"       " 325" " 80" " 80"  " 50"   " 40"     " 50"     
##  [119,] "Muk"               " 500" "105" "105"  " 75"   " 65"     "100"     
##  [120,] "Mega Muk"          " 500" "105" "105"  " 75"   " 65"     "100"     
##  [121,] "Shellder"          " 305" " 30" " 65"  "100"   " 45"     " 25"     
##  [122,] "Cloyster"          " 525" " 50" " 95"  "180"   " 85"     " 45"     
##  [123,] "Gastly"            " 310" " 30" " 35"  " 30"   "100"     " 35"     
##  [124,] "Haunter"           " 405" " 45" " 50"  " 45"   "115"     " 55"     
##  [125,] "Gengar"            " 500" " 60" " 65"  " 60"   "130"     " 75"     
##  [126,] "Mega Gengar"       " 600" " 60" " 65"  " 80"   "170"     " 95"     
##  [127,] "Onix"              " 385" " 35" " 45"  "160"   " 30"     " 45"     
##  [128,] "Drowzee"           " 328" " 60" " 48"  " 45"   " 43"     " 90"     
##  [129,] "Hypno"             " 483" " 85" " 73"  " 70"   " 73"     "115"     
##  [130,] "Krabby"            " 325" " 30" "105"  " 90"   " 25"     " 25"     
##  [131,] "Kingler"           " 475" " 55" "130"  "115"   " 50"     " 50"     
##  [132,] "Voltorb"           " 330" " 40" " 30"  " 50"   " 55"     " 55"     
##  [133,] "Electrode"         " 490" " 60" " 50"  " 70"   " 80"     " 80"     
##  [134,] "Exeggcute"         " 325" " 60" " 40"  " 80"   " 60"     " 45"     
##  [135,] "Exeggutor"         " 530" " 95" " 95"  " 85"   "125"     " 75"     
##  [136,] "Mega Exeggutor"    " 530" " 95" "105"  " 85"   "125"     " 75"     
##  [137,] "Cubone"            " 320" " 50" " 50"  " 95"   " 40"     " 50"     
##  [138,] "Marowak"           " 425" " 60" " 80"  "110"   " 50"     " 80"     
##  [139,] "Mega Marowak"      " 425" " 60" " 80"  "110"   " 50"     " 80"     
##  [140,] "Hitmonlee"         " 455" " 50" "120"  " 53"   " 35"     "110"     
##  [141,] "Hitmonchan"        " 455" " 50" "105"  " 79"   " 35"     "110"     
##  [142,] "Lickitung"         " 385" " 90" " 55"  " 75"   " 60"     " 75"     
##  [143,] "Koffing"           " 340" " 40" " 65"  " 95"   " 60"     " 45"     
##  [144,] "Weezing"           " 490" " 65" " 90"  "120"   " 85"     " 70"     
##  [145,] "Mega Weezing"      " 490" " 65" " 90"  "120"   " 85"     " 70"     
##  [146,] "Rhyhorn"           " 345" " 80" " 85"  " 95"   " 30"     " 30"     
##  [147,] "Rhydon"            " 485" "105" "130"  "120"   " 45"     " 45"     
##  [148,] "Chansey"           " 450" "250" "  5"  "  5"   " 35"     "105"     
##  [149,] "Tangela"           " 435" " 65" " 55"  "115"   "100"     " 40"     
##  [150,] "Kangaskhan"        " 490" "105" " 95"  " 80"   " 40"     " 80"     
##  [151,] "Mega Kangaskhan"   " 590" "105" "125"  "100"   " 60"     "100"     
##  [152,] "Horsea"            " 295" " 30" " 40"  " 70"   " 70"     " 25"     
##  [153,] "Seadra"            " 440" " 55" " 65"  " 95"   " 95"     " 45"     
##  [154,] "Goldeen"           " 320" " 45" " 67"  " 60"   " 35"     " 50"     
##  [155,] "Seaking"           " 450" " 80" " 92"  " 65"   " 65"     " 80"     
##  [156,] "Staryu"            " 340" " 30" " 45"  " 55"   " 70"     " 55"     
##  [157,] "Starmie"           " 520" " 60" " 75"  " 85"   "100"     " 85"     
##  [158,] "Mr. Mime"          " 460" " 40" " 45"  " 65"   "100"     "120"     
##  [159,] "Mega Mr. Mime"     " 460" " 50" " 65"  " 65"   " 90"     " 90"     
##  [160,] "Scyther"           " 500" " 70" "110"  " 80"   " 55"     " 80"     
##  [161,] "Jynx"              " 455" " 65" " 50"  " 35"   "115"     " 95"     
##  [162,] "Electabuzz"        " 490" " 65" " 83"  " 57"   " 95"     " 85"     
##  [163,] "Magmar"            " 495" " 65" " 95"  " 57"   "100"     " 85"     
##  [164,] "Pinsir"            " 500" " 65" "125"  "100"   " 55"     " 70"     
##  [165,] "Mega Pinsir"       " 600" " 65" "155"  "120"   " 65"     " 90"     
##  [166,] "Tauros"            " 490" " 75" "100"  " 95"   " 40"     " 70"     
##  [167,] "Magikarp"          " 200" " 20" " 10"  " 55"   " 15"     " 20"     
##  [168,] "Gyarados"          " 540" " 95" "125"  " 79"   " 60"     "100"     
##  [169,] "Mega Gyarados"     " 640" " 95" "155"  "109"   " 70"     "130"     
##  [170,] "Lapras"            " 535" "130" " 85"  " 80"   " 85"     " 95"     
##  [171,] "Ditto"             " 288" " 48" " 48"  " 48"   " 48"     " 48"     
##  [172,] "Eevee"             " 325" " 55" " 55"  " 50"   " 45"     " 65"     
##  [173,] "Mega Eevee"        " 435" " 65" " 75"  " 70"   " 65"     " 85"     
##  [174,] "Vaporeon"          " 525" "130" " 65"  " 60"   "110"     " 95"     
##  [175,] "Jolteon"           " 525" " 65" " 65"  " 60"   "110"     " 95"     
##  [176,] "Flareon"           " 525" " 65" "130"  " 60"   " 95"     "110"     
##  [177,] "Porygon"           " 395" " 65" " 60"  " 70"   " 85"     " 75"     
##  [178,] "Omanyte"           " 355" " 35" " 40"  "100"   " 90"     " 55"     
##  [179,] "Omastar"           " 495" " 70" " 60"  "125"   "115"     " 70"     
##  [180,] "Kabuto"            " 355" " 30" " 80"  " 90"   " 55"     " 45"     
##  [181,] "Kabutops"          " 495" " 60" "115"  "105"   " 65"     " 70"     
##  [182,] "Aerodactyl"        " 515" " 80" "105"  " 65"   " 60"     " 75"     
##  [183,] "Mega Aerodactyl"   " 615" " 80" "135"  " 85"   " 70"     " 95"     
##  [184,] "Snorlax"           " 540" "160" "110"  " 65"   " 65"     "110"     
##  [185,] "Articuno"          " 580" " 90" " 85"  "100"   " 95"     "125"     
##  [186,] "Mega Articuno"     " 580" " 90" " 85"  " 85"   "125"     "100"     
##  [187,] "Zapdos"            " 580" " 90" " 90"  " 85"   "125"     " 90"     
##  [188,] "Mega Zapdos"       " 580" " 90" "125"  " 90"   " 85"     " 90"     
##  [189,] "Moltres"           " 580" " 90" "100"  " 90"   "125"     " 85"     
##  [190,] "Mega Moltres"      " 580" " 90" " 85"  " 90"   "100"     "125"     
##  [191,] "Dratini"           " 300" " 41" " 64"  " 45"   " 50"     " 50"     
##  [192,] "Dragonair"         " 420" " 61" " 84"  " 65"   " 70"     " 70"     
##  [193,] "Dragonite"         " 600" " 91" "134"  " 95"   "100"     "100"     
##  [194,] "Mewtwo"            " 680" "106" "110"  " 90"   "154"     " 90"     
##  [195,] "Mega Mewtwo"       " 780" "106" "190"  "100"   "154"     "100"     
##  [196,] "Mega Mewtwo X"     " 780" "106" "150"  " 70"   "194"     "120"     
##  [197,] "Mew"               " 600" "100" "100"  "100"   "100"     "100"     
##  [198,] "Chikorita"         " 318" " 45" " 49"  " 65"   " 49"     " 65"     
##  [199,] "Bayleef"           " 405" " 60" " 62"  " 80"   " 63"     " 80"     
##  [200,] "Meganium"          " 525" " 80" " 82"  "100"   " 83"     "100"     
##  [201,] "Cyndaquil"         " 309" " 39" " 52"  " 43"   " 60"     " 50"     
##  [202,] "Quilava"           " 405" " 58" " 64"  " 58"   " 80"     " 65"     
##  [203,] "Typhlosion"        " 534" " 78" " 84"  " 78"   "109"     " 85"     
##  [204,] "Totodile"          " 314" " 50" " 65"  " 64"   " 44"     " 48"     
##  [205,] "Croconaw"          " 405" " 65" " 80"  " 80"   " 59"     " 63"     
##  [206,] "Feraligatr"        " 530" " 85" "105"  "100"   " 79"     " 83"     
##  [207,] "Sentret"           " 215" " 35" " 46"  " 34"   " 35"     " 45"     
##  [208,] "Furret"            " 415" " 85" " 76"  " 64"   " 45"     " 55"     
##  [209,] "Hoothoot"          " 262" " 60" " 30"  " 30"   " 36"     " 56"     
##  [210,] "Noctowl"           " 452" "100" " 50"  " 50"   " 86"     " 96"     
##  [211,] "Ledyba"            " 265" " 40" " 20"  " 30"   " 40"     " 80"     
##  [212,] "Ledian"            " 390" " 55" " 35"  " 50"   " 55"     "110"     
##  [213,] "Spinarak"          " 250" " 40" " 60"  " 40"   " 40"     " 40"     
##  [214,] "Ariados"           " 400" " 70" " 90"  " 70"   " 60"     " 70"     
##  [215,] "Crobat"            " 535" " 85" " 90"  " 80"   " 70"     " 80"     
##  [216,] "Chinchou"          " 330" " 75" " 38"  " 38"   " 56"     " 56"     
##  [217,] "Lanturn"           " 460" "125" " 58"  " 58"   " 76"     " 76"     
##  [218,] "Pichu"             " 205" " 20" " 40"  " 15"   " 35"     " 35"     
##  [219,] "Cleffa"            " 218" " 50" " 25"  " 28"   " 45"     " 55"     
##  [220,] "Igglybuff"         " 210" " 90" " 30"  " 15"   " 40"     " 20"     
##  [221,] "Togepi"            " 245" " 35" " 20"  " 65"   " 40"     " 65"     
##  [222,] "Togetic"           " 405" " 55" " 40"  " 85"   " 80"     "105"     
##  [223,] "Natu"              " 320" " 40" " 50"  " 45"   " 70"     " 45"     
##  [224,] "Xatu"              " 470" " 65" " 75"  " 70"   " 95"     " 70"     
##  [225,] "Mareep"            " 280" " 55" " 40"  " 40"   " 65"     " 45"     
##  [226,] "Flaaffy"           " 365" " 70" " 55"  " 55"   " 80"     " 60"     
##  [227,] "Ampharos"          " 510" " 90" " 75"  " 85"   "115"     " 90"     
##  [228,] "Mega Ampharos"     " 610" " 90" " 95"  "105"   "165"     "110"     
##  [229,] "Bellossom"         " 490" " 75" " 80"  " 95"   " 90"     "100"     
##  [230,] "Marill"            " 250" " 70" " 20"  " 50"   " 20"     " 50"     
##  [231,] "Azumarill"         " 420" "100" " 50"  " 80"   " 60"     " 80"     
##  [232,] "Sudowoodo"         " 410" " 70" "100"  "115"   " 30"     " 65"     
##  [233,] "Politoed"          " 500" " 90" " 75"  " 75"   " 90"     "100"     
##  [234,] "Hoppip"            " 250" " 35" " 35"  " 40"   " 35"     " 55"     
##  [235,] "Skiploom"          " 340" " 55" " 45"  " 50"   " 45"     " 65"     
##  [236,] "Jumpluff"          " 460" " 75" " 55"  " 70"   " 55"     " 95"     
##  [237,] "Aipom"             " 360" " 55" " 70"  " 55"   " 40"     " 55"     
##  [238,] "Sunkern"           " 180" " 30" " 30"  " 30"   " 30"     " 30"     
##  [239,] "Sunflora"          " 425" " 75" " 75"  " 55"   "105"     " 85"     
##  [240,] "Yanma"             " 390" " 65" " 65"  " 45"   " 75"     " 45"     
##  [241,] "Wooper"            " 210" " 55" " 45"  " 45"   " 25"     " 25"     
##  [242,] "Quagsire"          " 430" " 95" " 85"  " 85"   " 65"     " 65"     
##  [243,] "Espeon"            " 525" " 65" " 65"  " 60"   "130"     " 95"     
##  [244,] "Umbreon"           " 525" " 95" " 65"  "110"   " 60"     "130"     
##  [245,] "Murkrow"           " 405" " 60" " 85"  " 42"   " 85"     " 42"     
##  [246,] "Slowking"          " 490" " 95" " 75"  " 80"   "100"     "110"     
##  [247,] "Mega Slowking"     " 490" " 95" " 65"  " 80"   "110"     "110"     
##  [248,] "Misdreavus"        " 435" " 60" " 60"  " 60"   " 85"     " 85"     
##  [249,] "Unown"             " 336" " 48" " 72"  " 48"   " 72"     " 48"     
##  [250,] "Wobbuffet"         " 405" "190" " 33"  " 58"   " 33"     " 58"     
##  [251,] "Girafarig"         " 455" " 70" " 80"  " 65"   " 90"     " 65"     
##  [252,] "Pineco"            " 290" " 50" " 65"  " 90"   " 35"     " 35"     
##  [253,] "Forretress"        " 465" " 75" " 90"  "140"   " 60"     " 60"     
##  [254,] "Dunsparce"         " 415" "100" " 70"  " 70"   " 65"     " 65"     
##  [255,] "Gligar"            " 430" " 65" " 75"  "105"   " 35"     " 65"     
##  [256,] "Steelix"           " 510" " 75" " 85"  "200"   " 55"     " 65"     
##  [257,] "Mega Steelix"      " 610" " 75" "125"  "230"   " 55"     " 95"     
##  [258,] "Snubbull"          " 300" " 60" " 80"  " 50"   " 40"     " 40"     
##  [259,] "Granbull"          " 450" " 90" "120"  " 75"   " 60"     " 60"     
##  [260,] "Qwilfish"          " 440" " 65" " 95"  " 85"   " 55"     " 55"     
##  [261,] "Scizor"            " 500" " 70" "130"  "100"   " 55"     " 80"     
##  [262,] "Mega Scizor"       " 600" " 70" "150"  "140"   " 65"     "100"     
##  [263,] "Shuckle"           " 505" " 20" " 10"  "230"   " 10"     "230"     
##  [264,] "Heracross"         " 500" " 80" "125"  " 75"   " 40"     " 95"     
##  [265,] "Mega Heracross"    " 600" " 80" "185"  "115"   " 40"     "105"     
##  [266,] "Sneasel"           " 430" " 55" " 95"  " 55"   " 35"     " 75"     
##  [267,] "Teddiursa"         " 330" " 60" " 80"  " 50"   " 50"     " 50"     
##  [268,] "Ursaring"          " 500" " 90" "130"  " 75"   " 75"     " 75"     
##  [269,] "Slugma"            " 250" " 40" " 40"  " 40"   " 70"     " 40"     
##  [270,] "Magcargo"          " 430" " 60" " 50"  "120"   " 90"     " 80"     
##  [271,] "Swinub"            " 250" " 50" " 50"  " 40"   " 30"     " 30"     
##  [272,] "Piloswine"         " 450" "100" "100"  " 80"   " 60"     " 60"     
##  [273,] "Corsola"           " 410" " 65" " 55"  " 95"   " 65"     " 95"     
##  [274,] "Mega Corsola"      " 410" " 60" " 55"  "100"   " 65"     "100"     
##  [275,] "Remoraid"          " 300" " 35" " 65"  " 35"   " 65"     " 35"     
##  [276,] "Octillery"         " 480" " 75" "105"  " 75"   "105"     " 75"     
##  [277,] "Delibird"          " 330" " 45" " 55"  " 45"   " 65"     " 45"     
##  [278,] "Mantine"           " 485" " 85" " 40"  " 70"   " 80"     "140"     
##  [279,] "Skarmory"          " 465" " 65" " 80"  "140"   " 40"     " 70"     
##  [280,] "Houndour"          " 330" " 45" " 60"  " 30"   " 80"     " 50"     
##  [281,] "Houndoom"          " 500" " 75" " 90"  " 50"   "110"     " 80"     
##  [282,] "Mega Houndoom"     " 600" " 75" " 90"  " 90"   "140"     " 90"     
##  [283,] "Kingdra"           " 540" " 75" " 95"  " 95"   " 95"     " 95"     
##  [284,] "Phanpy"            " 330" " 90" " 60"  " 60"   " 40"     " 40"     
##  [285,] "Donphan"           " 500" " 90" "120"  "120"   " 60"     " 60"     
##  [286,] "Porygon2"          " 515" " 85" " 80"  " 90"   "105"     " 95"     
##  [287,] "Stantler"          " 465" " 73" " 95"  " 62"   " 85"     " 65"     
##  [288,] "Smeargle"          " 250" " 55" " 20"  " 35"   " 20"     " 45"     
##  [289,] "Tyrogue"           " 210" " 35" " 35"  " 35"   " 35"     " 35"     
##  [290,] "Hitmontop"         " 455" " 50" " 95"  " 95"   " 35"     "110"     
##  [291,] "Smoochum"          " 305" " 45" " 30"  " 15"   " 85"     " 65"     
##  [292,] "Elekid"            " 360" " 45" " 63"  " 37"   " 65"     " 55"     
##  [293,] "Magby"             " 365" " 45" " 75"  " 37"   " 70"     " 55"     
##  [294,] "Miltank"           " 490" " 95" " 80"  "105"   " 40"     " 70"     
##  [295,] "Blissey"           " 540" "255" " 10"  " 10"   " 75"     "135"     
##  [296,] "Raikou"            " 580" " 90" " 85"  " 75"   "115"     "100"     
##  [297,] "Entei"             " 580" "115" "115"  " 85"   " 90"     " 75"     
##  [298,] "Suicune"           " 580" "100" " 75"  "115"   " 90"     "115"     
##  [299,] "Larvitar"          " 300" " 50" " 64"  " 50"   " 45"     " 50"     
##  [300,] "Pupitar"           " 410" " 70" " 84"  " 70"   " 65"     " 70"     
##  [301,] "Tyranitar"         " 600" "100" "134"  "110"   " 95"     "100"     
##  [302,] "Mega Tyranitar"    " 700" "100" "164"  "150"   " 95"     "120"     
##  [303,] "Lugia"             " 680" "106" " 90"  "130"   " 90"     "154"     
##  [304,] "Ho-oh"             " 680" "106" "130"  " 90"   "110"     "154"     
##  [305,] "Celebi"            " 600" "100" "100"  "100"   "100"     "100"     
##  [306,] "Treecko"           " 310" " 40" " 45"  " 35"   " 65"     " 55"     
##  [307,] "Grovyle"           " 405" " 50" " 65"  " 45"   " 85"     " 65"     
##  [308,] "Sceptile"          " 530" " 70" " 85"  " 65"   "105"     " 85"     
##  [309,] "Mega Sceptile"     " 630" " 70" "110"  " 75"   "145"     " 85"     
##  [310,] "Torchic"           " 310" " 45" " 60"  " 40"   " 70"     " 50"     
##  [311,] "Combusken"         " 405" " 60" " 85"  " 60"   " 85"     " 60"     
##  [312,] "Blaziken"          " 530" " 80" "120"  " 70"   "110"     " 70"     
##  [313,] "Mega Blaziken"     " 630" " 80" "160"  " 80"   "130"     " 80"     
##  [314,] "Mudkip"            " 310" " 50" " 70"  " 50"   " 50"     " 50"     
##  [315,] "Marshtomp"         " 405" " 70" " 85"  " 70"   " 60"     " 70"     
##  [316,] "Swampert"          " 535" "100" "110"  " 90"   " 85"     " 90"     
##  [317,] "Mega Swampert"     " 635" "100" "150"  "110"   " 95"     "110"     
##  [318,] "Poochyena"         " 220" " 35" " 55"  " 35"   " 30"     " 30"     
##  [319,] "Mightyena"         " 420" " 70" " 90"  " 70"   " 60"     " 60"     
##  [320,] "Zigzagoon"         " 240" " 38" " 30"  " 41"   " 30"     " 41"     
##  [321,] "Mega Zigzagoon"    " 240" " 38" " 30"  " 41"   " 30"     " 41"     
##  [322,] "Linoone"           " 420" " 78" " 70"  " 61"   " 50"     " 61"     
##  [323,] "Mega Linoone"      " 420" " 78" " 70"  " 61"   " 50"     " 61"     
##  [324,] "Wurmple"           " 195" " 45" " 45"  " 35"   " 20"     " 30"     
##  [325,] "Silcoon"           " 205" " 50" " 35"  " 55"   " 25"     " 25"     
##  [326,] "Beautifly"         " 395" " 60" " 70"  " 50"   "100"     " 50"     
##  [327,] "Cascoon"           " 205" " 50" " 35"  " 55"   " 25"     " 25"     
##  [328,] "Dustox"            " 385" " 60" " 50"  " 70"   " 50"     " 90"     
##  [329,] "Lotad"             " 220" " 40" " 30"  " 30"   " 40"     " 50"     
##  [330,] "Lombre"            " 340" " 60" " 50"  " 50"   " 60"     " 70"     
##  [331,] "Ludicolo"          " 480" " 80" " 70"  " 70"   " 90"     "100"     
##  [332,] "Seedot"            " 220" " 40" " 40"  " 50"   " 30"     " 30"     
##  [333,] "Nuzleaf"           " 340" " 70" " 70"  " 40"   " 60"     " 40"     
##  [334,] "Shiftry"           " 480" " 90" "100"  " 60"   " 90"     " 60"     
##  [335,] "Taillow"           " 270" " 40" " 55"  " 30"   " 30"     " 30"     
##  [336,] "Swellow"           " 455" " 60" " 85"  " 60"   " 75"     " 50"     
##  [337,] "Wingull"           " 270" " 40" " 30"  " 30"   " 55"     " 30"     
##  [338,] "Pelipper"          " 440" " 60" " 50"  "100"   " 95"     " 70"     
##  [339,] "Ralts"             " 198" " 28" " 25"  " 25"   " 45"     " 35"     
##  [340,] "Kirlia"            " 278" " 38" " 35"  " 35"   " 65"     " 55"     
##  [341,] "Gardevoir"         " 518" " 68" " 65"  " 65"   "125"     "115"     
##  [342,] "Mega Gardevoir"    " 618" " 68" " 85"  " 65"   "165"     "135"     
##  [343,] "Surskit"           " 269" " 40" " 30"  " 32"   " 50"     " 52"     
##  [344,] "Masquerain"        " 454" " 70" " 60"  " 62"   "100"     " 82"     
##  [345,] "Shroomish"         " 295" " 60" " 40"  " 60"   " 40"     " 60"     
##  [346,] "Breloom"           " 460" " 60" "130"  " 80"   " 60"     " 60"     
##  [347,] "Slakoth"           " 280" " 60" " 60"  " 60"   " 35"     " 35"     
##  [348,] "Vigoroth"          " 440" " 80" " 80"  " 80"   " 55"     " 55"     
##  [349,] "Slaking"           " 670" "150" "160"  "100"   " 95"     " 65"     
##  [350,] "Nincada"           " 266" " 31" " 45"  " 90"   " 30"     " 30"     
##  [351,] "Ninjask"           " 456" " 61" " 90"  " 45"   " 50"     " 50"     
##  [352,] "Shedinja"          " 236" "  1" " 90"  " 45"   " 30"     " 30"     
##  [353,] "Whismur"           " 240" " 64" " 51"  " 23"   " 51"     " 23"     
##  [354,] "Loudred"           " 360" " 84" " 71"  " 43"   " 71"     " 43"     
##  [355,] "Exploud"           " 490" "104" " 91"  " 63"   " 91"     " 73"     
##  [356,] "Makuhita"          " 237" " 72" " 60"  " 30"   " 20"     " 30"     
##  [357,] "Hariyama"          " 474" "144" "120"  " 60"   " 40"     " 60"     
##  [358,] "Azurill"           " 190" " 50" " 20"  " 40"   " 20"     " 40"     
##  [359,] "Nosepass"          " 375" " 30" " 45"  "135"   " 45"     " 90"     
##  [360,] "Skitty"            " 260" " 50" " 45"  " 45"   " 35"     " 35"     
##  [361,] "Delcatty"          " 400" " 70" " 65"  " 65"   " 55"     " 55"     
##  [362,] "Sableye"           " 380" " 50" " 75"  " 75"   " 65"     " 65"     
##  [363,] "Mega Sableye"      " 480" " 50" " 85"  "125"   " 85"     "115"     
##  [364,] "Mawile"            " 380" " 50" " 85"  " 85"   " 55"     " 55"     
##  [365,] "Mega Mawile"       " 480" " 50" "105"  "125"   " 55"     " 95"     
##  [366,] "Aron"              " 330" " 50" " 70"  "100"   " 40"     " 40"     
##  [367,] "Lairon"            " 430" " 60" " 90"  "140"   " 50"     " 50"     
##  [368,] "Aggron"            " 530" " 70" "110"  "180"   " 60"     " 60"     
##  [369,] "Mega Aggron"       " 630" " 70" "140"  "230"   " 60"     " 80"     
##  [370,] "Meditite"          " 280" " 30" " 40"  " 55"   " 40"     " 55"     
##  [371,] "Medicham"          " 410" " 60" " 60"  " 75"   " 60"     " 75"     
##  [372,] "Mega Medicham"     " 510" " 60" "100"  " 85"   " 80"     " 85"     
##  [373,] "Electrike"         " 295" " 40" " 45"  " 40"   " 65"     " 40"     
##  [374,] "Manectric"         " 475" " 70" " 75"  " 60"   "105"     " 60"     
##  [375,] "Mega Manectric"    " 575" " 70" " 75"  " 80"   "135"     " 80"     
##  [376,] "Plusle"            " 405" " 60" " 50"  " 40"   " 85"     " 75"     
##  [377,] "Minun"             " 405" " 60" " 40"  " 50"   " 75"     " 85"     
##  [378,] "Volbeat"           " 430" " 65" " 73"  " 75"   " 47"     " 85"     
##  [379,] "Illumise"          " 430" " 65" " 47"  " 75"   " 73"     " 85"     
##  [380,] "Roselia"           " 400" " 50" " 60"  " 45"   "100"     " 80"     
##  [381,] "Gulpin"            " 302" " 70" " 43"  " 53"   " 43"     " 53"     
##  [382,] "Swalot"            " 467" "100" " 73"  " 83"   " 73"     " 83"     
##  [383,] "Carvanha"          " 305" " 45" " 90"  " 20"   " 65"     " 20"     
##  [384,] "Sharpedo"          " 460" " 70" "120"  " 40"   " 95"     " 40"     
##  [385,] "Mega Sharpedo"     " 560" " 70" "140"  " 70"   "110"     " 65"     
##  [386,] "Wailmer"           " 400" "130" " 70"  " 35"   " 70"     " 35"     
##  [387,] "Wailord"           " 500" "170" " 90"  " 45"   " 90"     " 45"     
##  [388,] "Numel"             " 305" " 60" " 60"  " 40"   " 65"     " 45"     
##  [389,] "Camerupt"          " 460" " 70" "100"  " 70"   "105"     " 75"     
##  [390,] "Mega Camerupt"     " 560" " 70" "120"  "100"   "145"     "105"     
##  [391,] "Torkoal"           " 470" " 70" " 85"  "140"   " 85"     " 70"     
##  [392,] "Spoink"            " 330" " 60" " 25"  " 35"   " 70"     " 80"     
##  [393,] "Grumpig"           " 470" " 80" " 45"  " 65"   " 90"     "110"     
##  [394,] "Spinda"            " 360" " 60" " 60"  " 60"   " 60"     " 60"     
##  [395,] "Trapinch"          " 290" " 45" "100"  " 45"   " 45"     " 45"     
##  [396,] "Vibrava"           " 340" " 50" " 70"  " 50"   " 50"     " 50"     
##  [397,] "Flygon"            " 520" " 80" "100"  " 80"   " 80"     " 80"     
##  [398,] "Cacnea"            " 335" " 50" " 85"  " 40"   " 85"     " 40"     
##  [399,] "Cacturne"          " 475" " 70" "115"  " 60"   "115"     " 60"     
##  [400,] "Swablu"            " 310" " 45" " 40"  " 60"   " 40"     " 75"     
##  [401,] "Altaria"           " 490" " 75" " 70"  " 90"   " 70"     "105"     
##  [402,] "Mega Altaria"      " 590" " 75" "110"  "110"   "110"     "105"     
##  [403,] "Zangoose"          " 458" " 73" "115"  " 60"   " 60"     " 60"     
##  [404,] "Seviper"           " 458" " 73" "100"  " 60"   "100"     " 60"     
##  [405,] "Lunatone"          " 460" " 90" " 55"  " 65"   " 95"     " 85"     
##  [406,] "Solrock"           " 460" " 90" " 95"  " 85"   " 55"     " 65"     
##  [407,] "Barboach"          " 288" " 50" " 48"  " 43"   " 46"     " 41"     
##  [408,] "Whiscash"          " 468" "110" " 78"  " 73"   " 76"     " 71"     
##  [409,] "Corphish"          " 308" " 43" " 80"  " 65"   " 50"     " 35"     
##  [410,] "Crawdaunt"         " 468" " 63" "120"  " 85"   " 90"     " 55"     
##  [411,] "Baltoy"            " 300" " 40" " 40"  " 55"   " 40"     " 70"     
##  [412,] "Claydol"           " 500" " 60" " 70"  "105"   " 70"     "120"     
##  [413,] "Lileep"            " 355" " 66" " 41"  " 77"   " 61"     " 87"     
##  [414,] "Cradily"           " 495" " 86" " 81"  " 97"   " 81"     "107"     
##  [415,] "Anorith"           " 355" " 45" " 95"  " 50"   " 40"     " 50"     
##  [416,] "Armaldo"           " 495" " 75" "125"  "100"   " 70"     " 80"     
##  [417,] "Feebas"            " 200" " 20" " 15"  " 20"   " 10"     " 55"     
##  [418,] "Milotic"           " 540" " 95" " 60"  " 79"   "100"     "125"     
##  [419,] "Castform"          " 420" " 70" " 70"  " 70"   " 70"     " 70"     
##  [420,] "Mega Castform"     " 420" " 70" " 70"  " 70"   " 70"     " 70"     
##  [421,] "Mega Castform X"   " 420" " 70" " 70"  " 70"   " 70"     " 70"     
##  [422,] "Mega Castform X"   " 420" " 70" " 70"  " 70"   " 70"     " 70"     
##  [423,] "Kecleon"           " 440" " 60" " 90"  " 70"   " 60"     "120"     
##  [424,] "Shuppet"           " 295" " 44" " 75"  " 35"   " 63"     " 33"     
##  [425,] "Banette"           " 455" " 64" "115"  " 65"   " 83"     " 63"     
##  [426,] "Mega Banette"      " 555" " 64" "165"  " 75"   " 93"     " 83"     
##  [427,] "Duskull"           " 295" " 20" " 40"  " 90"   " 30"     " 90"     
##  [428,] "Dusclops"          " 455" " 40" " 70"  "130"   " 60"     "130"     
##  [429,] "Tropius"           " 460" " 99" " 68"  " 83"   " 72"     " 87"     
##  [430,] "Chimecho"          " 455" " 75" " 50"  " 80"   " 95"     " 90"     
##  [431,] "Absol"             " 465" " 65" "130"  " 60"   " 75"     " 60"     
##  [432,] "Mega Absol"        " 565" " 65" "150"  " 60"   "115"     " 60"     
##  [433,] "Wynaut"            " 260" " 95" " 23"  " 48"   " 23"     " 48"     
##  [434,] "Snorunt"           " 300" " 50" " 50"  " 50"   " 50"     " 50"     
##  [435,] "Glalie"            " 480" " 80" " 80"  " 80"   " 80"     " 80"     
##  [436,] "Mega Glalie"       " 580" " 80" "120"  " 80"   "120"     " 80"     
##  [437,] "Spheal"            " 290" " 70" " 40"  " 50"   " 55"     " 50"     
##  [438,] "Sealeo"            " 410" " 90" " 60"  " 70"   " 75"     " 70"     
##  [439,] "Walrein"           " 530" "110" " 80"  " 90"   " 95"     " 90"     
##  [440,] "Clamperl"          " 345" " 35" " 64"  " 85"   " 74"     " 55"     
##  [441,] "Huntail"           " 485" " 55" "104"  "105"   " 94"     " 75"     
##  [442,] "Gorebyss"          " 485" " 55" " 84"  "105"   "114"     " 75"     
##  [443,] "Relicanth"         " 485" "100" " 90"  "130"   " 45"     " 65"     
##  [444,] "Luvdisc"           " 330" " 43" " 30"  " 55"   " 40"     " 65"     
##  [445,] "Bagon"             " 300" " 45" " 75"  " 60"   " 40"     " 30"     
##  [446,] "Shelgon"           " 420" " 65" " 95"  "100"   " 60"     " 50"     
##  [447,] "Salamence"         " 600" " 95" "135"  " 80"   "110"     " 80"     
##  [448,] "Mega Salamence"    " 700" " 95" "145"  "130"   "120"     " 90"     
##  [449,] "Beldum"            " 300" " 40" " 55"  " 80"   " 35"     " 60"     
##  [450,] "Metang"            " 420" " 60" " 75"  "100"   " 55"     " 80"     
##  [451,] "Metagross"         " 600" " 80" "135"  "130"   " 95"     " 90"     
##  [452,] "Mega Metagross"    " 700" " 80" "145"  "150"   "105"     "110"     
##  [453,] "Regirock"          " 580" " 80" "100"  "200"   " 50"     "100"     
##  [454,] "Regice"            " 580" " 80" " 50"  "100"   "100"     "200"     
##  [455,] "Registeel"         " 580" " 80" " 75"  "150"   " 75"     "150"     
##  [456,] "Latias"            " 600" " 80" " 80"  " 90"   "110"     "130"     
##  [457,] "Mega Latias"       " 700" " 80" "100"  "120"   "140"     "150"     
##  [458,] "Latios"            " 600" " 80" " 90"  " 80"   "130"     "110"     
##  [459,] "Mega Latios"       " 700" " 80" "130"  "100"   "160"     "120"     
##  [460,] "Kyogre"            " 670" "100" "100"  " 90"   "150"     "140"     
##  [461,] "Mega Kyogre"       " 770" "100" "150"  " 90"   "180"     "160"     
##  [462,] "Groudon"           " 670" "100" "150"  "140"   "100"     " 90"     
##  [463,] "Mega Groudon"      " 770" "100" "180"  "160"   "150"     " 90"     
##  [464,] "Rayquaza"          " 680" "105" "150"  " 90"   "150"     " 90"     
##  [465,] "Mega Rayquaza"     " 780" "105" "180"  "100"   "180"     "100"     
##  [466,] "Jirachi"           " 600" "100" "100"  "100"   "100"     "100"     
##  [467,] "Deoxys"            " 600" " 50" "150"  " 50"   "150"     " 50"     
##  [468,] "Mega Deoxys"       " 600" " 50" "180"  " 20"   "180"     " 20"     
##  [469,] "Mega Deoxys X"     " 600" " 50" " 70"  "160"   " 70"     "160"     
##  [470,] "Mega Deoxys X"     " 600" " 50" " 95"  " 90"   " 95"     " 90"     
##  [471,] "Turtwig"           " 318" " 55" " 68"  " 64"   " 45"     " 55"     
##  [472,] "Grotle"            " 405" " 75" " 89"  " 85"   " 55"     " 65"     
##  [473,] "Torterra"          " 525" " 95" "109"  "105"   " 75"     " 85"     
##  [474,] "Chimchar"          " 309" " 44" " 58"  " 44"   " 58"     " 44"     
##  [475,] "Monferno"          " 405" " 64" " 78"  " 52"   " 78"     " 52"     
##  [476,] "Infernape"         " 534" " 76" "104"  " 71"   "104"     " 71"     
##  [477,] "Piplup"            " 314" " 53" " 51"  " 53"   " 61"     " 56"     
##  [478,] "Prinplup"          " 405" " 64" " 66"  " 68"   " 81"     " 76"     
##  [479,] "Empoleon"          " 530" " 84" " 86"  " 88"   "111"     "101"     
##  [480,] "Starly"            " 245" " 40" " 55"  " 30"   " 30"     " 30"     
##  [481,] "Staravia"          " 340" " 55" " 75"  " 50"   " 40"     " 40"     
##  [482,] "Staraptor"         " 485" " 85" "120"  " 70"   " 50"     " 60"     
##  [483,] "Bidoof"            " 250" " 59" " 45"  " 40"   " 35"     " 40"     
##  [484,] "Bibarel"           " 410" " 79" " 85"  " 60"   " 55"     " 60"     
##  [485,] "Kricketot"         " 194" " 37" " 25"  " 41"   " 25"     " 41"     
##  [486,] "Kricketune"        " 384" " 77" " 85"  " 51"   " 55"     " 51"     
##  [487,] "Shinx"             " 263" " 45" " 65"  " 34"   " 40"     " 34"     
##  [488,] "Luxio"             " 363" " 60" " 85"  " 49"   " 60"     " 49"     
##  [489,] "Luxray"            " 523" " 80" "120"  " 79"   " 95"     " 79"     
##  [490,] "Budew"             " 280" " 40" " 30"  " 35"   " 50"     " 70"     
##  [491,] "Roserade"          " 515" " 60" " 70"  " 65"   "125"     "105"     
##  [492,] "Cranidos"          " 350" " 67" "125"  " 40"   " 30"     " 30"     
##  [493,] "Rampardos"         " 495" " 97" "165"  " 60"   " 65"     " 50"     
##  [494,] "Shieldon"          " 350" " 30" " 42"  "118"   " 42"     " 88"     
##  [495,] "Bastiodon"         " 495" " 60" " 52"  "168"   " 47"     "138"     
##  [496,] "Burmy"             " 224" " 40" " 29"  " 45"   " 29"     " 45"     
##  [497,] "Wormadam"          " 424" " 60" " 59"  " 85"   " 79"     "105"     
##  [498,] "Mega Wormadam"     " 424" " 60" " 79"  "105"   " 59"     " 85"     
##  [499,] "Mega Wormadam X"   " 424" " 60" " 69"  " 95"   " 69"     " 95"     
##  [500,] "Mothim"            " 424" " 70" " 94"  " 50"   " 94"     " 50"     
##  [501,] "Combee"            " 244" " 30" " 30"  " 42"   " 30"     " 42"     
##  [502,] "Vespiquen"         " 474" " 70" " 80"  "102"   " 80"     "102"     
##  [503,] "Pachirisu"         " 405" " 60" " 45"  " 70"   " 45"     " 90"     
##  [504,] "Buizel"            " 330" " 55" " 65"  " 35"   " 60"     " 30"     
##  [505,] "Floatzel"          " 495" " 85" "105"  " 55"   " 85"     " 50"     
##  [506,] "Cherubi"           " 275" " 45" " 35"  " 45"   " 62"     " 53"     
##  [507,] "Cherrim"           " 450" " 70" " 60"  " 70"   " 87"     " 78"     
##  [508,] "Shellos"           " 325" " 76" " 48"  " 48"   " 57"     " 62"     
##  [509,] "Gastrodon"         " 475" "111" " 83"  " 68"   " 92"     " 82"     
##  [510,] "Ambipom"           " 482" " 75" "100"  " 66"   " 60"     " 66"     
##  [511,] "Drifloon"          " 348" " 90" " 50"  " 34"   " 60"     " 44"     
##  [512,] "Drifblim"          " 498" "150" " 80"  " 44"   " 90"     " 54"     
##  [513,] "Buneary"           " 350" " 55" " 66"  " 44"   " 44"     " 56"     
##  [514,] "Lopunny"           " 480" " 65" " 76"  " 84"   " 54"     " 96"     
##  [515,] "Mega Lopunny"      " 580" " 65" "136"  " 94"   " 54"     " 96"     
##  [516,] "Mismagius"         " 495" " 60" " 60"  " 60"   "105"     "105"     
##  [517,] "Honchkrow"         " 505" "100" "125"  " 52"   "105"     " 52"     
##  [518,] "Glameow"           " 310" " 49" " 55"  " 42"   " 42"     " 37"     
##  [519,] "Purugly"           " 452" " 71" " 82"  " 64"   " 64"     " 59"     
##  [520,] "Chingling"         " 285" " 45" " 30"  " 50"   " 65"     " 50"     
##  [521,] "Stunky"            " 329" " 63" " 63"  " 47"   " 41"     " 41"     
##  [522,] "Skuntank"          " 479" "103" " 93"  " 67"   " 71"     " 61"     
##  [523,] "Bronzor"           " 300" " 57" " 24"  " 86"   " 24"     " 86"     
##  [524,] "Bronzong"          " 500" " 67" " 89"  "116"   " 79"     "116"     
##  [525,] "Bonsly"            " 290" " 50" " 80"  " 95"   " 10"     " 45"     
##  [526,] "Mime Jr."          " 310" " 20" " 25"  " 45"   " 70"     " 90"     
##  [527,] "Happiny"           " 220" "100" "  5"  "  5"   " 15"     " 65"     
##  [528,] "Chatot"            " 411" " 76" " 65"  " 45"   " 92"     " 42"     
##  [529,] "Spiritomb"         " 485" " 50" " 92"  "108"   " 92"     "108"     
##  [530,] "Gible"             " 300" " 58" " 70"  " 45"   " 40"     " 45"     
##  [531,] "Gabite"            " 410" " 68" " 90"  " 65"   " 50"     " 55"     
##  [532,] "Garchomp"          " 600" "108" "130"  " 95"   " 80"     " 85"     
##  [533,] "Mega Garchomp"     " 700" "108" "170"  "115"   "120"     " 95"     
##  [534,] "Munchlax"          " 390" "135" " 85"  " 40"   " 40"     " 85"     
##  [535,] "Riolu"             " 285" " 40" " 70"  " 40"   " 35"     " 40"     
##  [536,] "Lucario"           " 525" " 70" "110"  " 70"   "115"     " 70"     
##  [537,] "Mega Lucario"      " 625" " 70" "145"  " 88"   "140"     " 70"     
##  [538,] "Hippopotas"        " 330" " 68" " 72"  " 78"   " 38"     " 42"     
##  [539,] "Hippowdon"         " 525" "108" "112"  "118"   " 68"     " 72"     
##  [540,] "Skorupi"           " 330" " 40" " 50"  " 90"   " 30"     " 55"     
##  [541,] "Drapion"           " 500" " 70" " 90"  "110"   " 60"     " 75"     
##  [542,] "Croagunk"          " 300" " 48" " 61"  " 40"   " 61"     " 40"     
##  [543,] "Toxicroak"         " 490" " 83" "106"  " 65"   " 86"     " 65"     
##  [544,] "Carnivine"         " 454" " 74" "100"  " 72"   " 90"     " 72"     
##  [545,] "Finneon"           " 330" " 49" " 49"  " 56"   " 49"     " 61"     
##  [546,] "Lumineon"          " 460" " 69" " 69"  " 76"   " 69"     " 86"     
##  [547,] "Mantyke"           " 345" " 45" " 20"  " 50"   " 60"     "120"     
##  [548,] "Snover"            " 334" " 60" " 62"  " 50"   " 62"     " 60"     
##  [549,] "Abomasnow"         " 494" " 90" " 92"  " 75"   " 92"     " 85"     
##  [550,] "Mega Abomasnow"    " 594" " 90" "132"  "105"   "132"     "105"     
##  [551,] "Weavile"           " 510" " 70" "120"  " 65"   " 45"     " 85"     
##  [552,] "Magnezone"         " 535" " 70" " 70"  "115"   "130"     " 90"     
##  [553,] "Lickilicky"        " 515" "110" " 85"  " 95"   " 80"     " 95"     
##  [554,] "Rhyperior"         " 535" "115" "140"  "130"   " 55"     " 55"     
##  [555,] "Tangrowth"         " 535" "100" "100"  "125"   "110"     " 50"     
##  [556,] "Electivire"        " 540" " 75" "123"  " 67"   " 95"     " 85"     
##  [557,] "Magmortar"         " 540" " 75" " 95"  " 67"   "125"     " 95"     
##  [558,] "Togekiss"          " 545" " 85" " 50"  " 95"   "120"     "115"     
##  [559,] "Yanmega"           " 515" " 86" " 76"  " 86"   "116"     " 56"     
##  [560,] "Leafeon"           " 525" " 65" "110"  "130"   " 60"     " 65"     
##  [561,] "Glaceon"           " 525" " 65" " 60"  "110"   "130"     " 95"     
##  [562,] "Gliscor"           " 510" " 75" " 95"  "125"   " 45"     " 75"     
##  [563,] "Mamoswine"         " 530" "110" "130"  " 80"   " 70"     " 60"     
##  [564,] "Porygon-Z"         " 535" " 85" " 80"  " 70"   "135"     " 75"     
##  [565,] "Gallade"           " 518" " 68" "125"  " 65"   " 65"     "115"     
##  [566,] "Mega Gallade"      " 618" " 68" "165"  " 95"   " 65"     "115"     
##  [567,] "Probopass"         " 525" " 60" " 55"  "145"   " 75"     "150"     
##  [568,] "Dusknoir"          " 525" " 45" "100"  "135"   " 65"     "135"     
##  [569,] "Froslass"          " 480" " 70" " 80"  " 70"   " 80"     " 70"     
##  [570,] "Rotom"             " 440" " 50" " 50"  " 77"   " 95"     " 77"     
##  [571,] "Mega Rotom"        " 520" " 50" " 65"  "107"   "105"     "107"     
##  [572,] "Mega Rotom X"      " 520" " 50" " 65"  "107"   "105"     "107"     
##  [573,] "Mega Rotom X"      " 520" " 50" " 65"  "107"   "105"     "107"     
##  [574,] "Mega Rotom X"      " 520" " 50" " 65"  "107"   "105"     "107"     
##  [575,] "Mega Rotom X"      " 520" " 50" " 65"  "107"   "105"     "107"     
##  [576,] "Uxie"              " 580" " 75" " 75"  "130"   " 75"     "130"     
##  [577,] "Mesprit"           " 580" " 80" "105"  "105"   "105"     "105"     
##  [578,] "Azelf"             " 580" " 75" "125"  " 70"   "125"     " 70"     
##  [579,] "Dialga"            " 680" "100" "120"  "120"   "150"     "100"     
##  [580,] "Palkia"            " 680" " 90" "120"  "100"   "150"     "120"     
##  [581,] "Heatran"           " 600" " 91" " 90"  "106"   "130"     "106"     
##  [582,] "Regigigas"         " 670" "110" "160"  "110"   " 80"     "110"     
##  [583,] "Giratina"          " 680" "150" "100"  "120"   "100"     "120"     
##  [584,] "Mega Giratina"     " 680" "150" "120"  "100"   "120"     "100"     
##  [585,] "Cresselia"         " 600" "120" " 70"  "120"   " 75"     "130"     
##  [586,] "Phione"            " 480" " 80" " 80"  " 80"   " 80"     " 80"     
##  [587,] "Manaphy"           " 600" "100" "100"  "100"   "100"     "100"     
##  [588,] "Darkrai"           " 600" " 70" " 90"  " 90"   "135"     " 90"     
##  [589,] "Shaymin"           " 600" "100" "100"  "100"   "100"     "100"     
##  [590,] "Mega Shaymin"      " 600" "100" "103"  " 75"   "120"     " 75"     
##  [591,] "Arceus"            " 720" "120" "120"  "120"   "120"     "120"     
##  [592,] "Victini"           " 600" "100" "100"  "100"   "100"     "100"     
##  [593,] "Snivy"             " 308" " 45" " 45"  " 55"   " 45"     " 55"     
##  [594,] "Servine"           " 413" " 60" " 60"  " 75"   " 60"     " 75"     
##  [595,] "Serperior"         " 528" " 75" " 75"  " 95"   " 75"     " 95"     
##  [596,] "Tepig"             " 308" " 65" " 63"  " 45"   " 45"     " 45"     
##  [597,] "Pignite"           " 418" " 90" " 93"  " 55"   " 70"     " 55"     
##  [598,] "Emboar"            " 528" "110" "123"  " 65"   "100"     " 65"     
##  [599,] "Oshawott"          " 308" " 55" " 55"  " 45"   " 63"     " 45"     
##  [600,] "Dewott"            " 413" " 75" " 75"  " 60"   " 83"     " 60"     
##  [601,] "Samurott"          " 528" " 95" "100"  " 85"   "108"     " 70"     
##  [602,] "Patrat"            " 255" " 45" " 55"  " 39"   " 35"     " 39"     
##  [603,] "Watchog"           " 420" " 60" " 85"  " 69"   " 60"     " 69"     
##  [604,] "Lillipup"          " 275" " 45" " 60"  " 45"   " 25"     " 45"     
##  [605,] "Herdier"           " 370" " 65" " 80"  " 65"   " 35"     " 65"     
##  [606,] "Stoutland"         " 500" " 85" "110"  " 90"   " 45"     " 90"     
##  [607,] "Purrloin"          " 281" " 41" " 50"  " 37"   " 50"     " 37"     
##  [608,] "Liepard"           " 446" " 64" " 88"  " 50"   " 88"     " 50"     
##  [609,] "Pansage"           " 316" " 50" " 53"  " 48"   " 53"     " 48"     
##  [610,] "Simisage"          " 498" " 75" " 98"  " 63"   " 98"     " 63"     
##  [611,] "Pansear"           " 316" " 50" " 53"  " 48"   " 53"     " 48"     
##  [612,] "Simisear"          " 498" " 75" " 98"  " 63"   " 98"     " 63"     
##  [613,] "Panpour"           " 316" " 50" " 53"  " 48"   " 53"     " 48"     
##  [614,] "Simipour"          " 498" " 75" " 98"  " 63"   " 98"     " 63"     
##  [615,] "Munna"             " 292" " 76" " 25"  " 45"   " 67"     " 55"     
##  [616,] "Musharna"          " 487" "116" " 55"  " 85"   "107"     " 95"     
##  [617,] "Pidove"            " 264" " 50" " 55"  " 50"   " 36"     " 30"     
##  [618,] "Tranquill"         " 358" " 62" " 77"  " 62"   " 50"     " 42"     
##  [619,] "Unfezant"          " 488" " 80" "115"  " 80"   " 65"     " 55"     
##  [620,] "Blitzle"           " 295" " 45" " 60"  " 32"   " 50"     " 32"     
##  [621,] "Zebstrika"         " 497" " 75" "100"  " 63"   " 80"     " 63"     
##  [622,] "Roggenrola"        " 280" " 55" " 75"  " 85"   " 25"     " 25"     
##  [623,] "Boldore"           " 390" " 70" "105"  "105"   " 50"     " 40"     
##  [624,] "Gigalith"          " 515" " 85" "135"  "130"   " 60"     " 80"     
##  [625,] "Woobat"            " 323" " 65" " 45"  " 43"   " 55"     " 43"     
##  [626,] "Swoobat"           " 425" " 67" " 57"  " 55"   " 77"     " 55"     
##  [627,] "Drilbur"           " 328" " 60" " 85"  " 40"   " 30"     " 45"     
##  [628,] "Excadrill"         " 508" "110" "135"  " 60"   " 50"     " 65"     
##  [629,] "Audino"            " 445" "103" " 60"  " 86"   " 60"     " 86"     
##  [630,] "Mega Audino"       " 545" "103" " 60"  "126"   " 80"     "126"     
##  [631,] "Timburr"           " 305" " 75" " 80"  " 55"   " 25"     " 35"     
##  [632,] "Gurdurr"           " 405" " 85" "105"  " 85"   " 40"     " 50"     
##  [633,] "Conkeldurr"        " 505" "105" "140"  " 95"   " 55"     " 65"     
##  [634,] "Tympole"           " 294" " 50" " 50"  " 40"   " 50"     " 40"     
##  [635,] "Palpitoad"         " 384" " 75" " 65"  " 55"   " 65"     " 55"     
##  [636,] "Seismitoad"        " 509" "105" " 95"  " 75"   " 85"     " 75"     
##  [637,] "Throh"             " 465" "120" "100"  " 85"   " 30"     " 85"     
##  [638,] "Sawk"              " 465" " 75" "125"  " 75"   " 30"     " 75"     
##  [639,] "Sewaddle"          " 310" " 45" " 53"  " 70"   " 40"     " 60"     
##  [640,] "Swadloon"          " 380" " 55" " 63"  " 90"   " 50"     " 80"     
##  [641,] "Leavanny"          " 500" " 75" "103"  " 80"   " 70"     " 80"     
##  [642,] "Venipede"          " 260" " 30" " 45"  " 59"   " 30"     " 39"     
##  [643,] "Whirlipede"        " 360" " 40" " 55"  " 99"   " 40"     " 79"     
##  [644,] "Scolipede"         " 485" " 60" "100"  " 89"   " 55"     " 69"     
##  [645,] "Cottonee"          " 280" " 40" " 27"  " 60"   " 37"     " 50"     
##  [646,] "Whimsicott"        " 480" " 60" " 67"  " 85"   " 77"     " 75"     
##  [647,] "Petilil"           " 280" " 45" " 35"  " 50"   " 70"     " 50"     
##  [648,] "Lilligant"         " 480" " 70" " 60"  " 75"   "110"     " 75"     
##  [649,] "Basculin"          " 460" " 70" " 92"  " 65"   " 80"     " 55"     
##  [650,] "Mega Basculin"     " 460" " 70" " 92"  " 65"   " 80"     " 55"     
##  [651,] "Sandile"           " 292" " 50" " 72"  " 35"   " 35"     " 35"     
##  [652,] "Krokorok"          " 351" " 60" " 82"  " 45"   " 45"     " 45"     
##  [653,] "Krookodile"        " 519" " 95" "117"  " 80"   " 65"     " 70"     
##  [654,] "Darumaka"          " 315" " 70" " 90"  " 45"   " 15"     " 45"     
##  [655,] "Mega Darumaka"     " 315" " 70" " 90"  " 45"   " 15"     " 45"     
##  [656,] "Darmanitan"        " 480" "105" "140"  " 55"   " 30"     " 55"     
##  [657,] "Mega Darmanitan"   " 540" "105" " 30"  "105"   "140"     "105"     
##  [658,] "Mega Darmanitan X" " 480" "105" "140"  " 55"   " 30"     " 55"     
##  [659,] "Mega Darmanitan X" " 540" "105" "160"  " 55"   " 30"     " 55"     
##  [660,] "Maractus"          " 461" " 75" " 86"  " 67"   "106"     " 67"     
##  [661,] "Dwebble"           " 325" " 50" " 65"  " 85"   " 35"     " 35"     
##  [662,] "Crustle"           " 485" " 70" "105"  "125"   " 65"     " 75"     
##  [663,] "Scraggy"           " 348" " 50" " 75"  " 70"   " 35"     " 70"     
##  [664,] "Scrafty"           " 488" " 65" " 90"  "115"   " 45"     "115"     
##  [665,] "Sigilyph"          " 490" " 72" " 58"  " 80"   "103"     " 80"     
##  [666,] "Yamask"            " 303" " 38" " 30"  " 85"   " 55"     " 65"     
##  [667,] "Mega Yamask"       " 303" " 38" " 55"  " 85"   " 30"     " 65"     
##  [668,] "Cofagrigus"        " 483" " 58" " 50"  "145"   " 95"     "105"     
##  [669,] "Tirtouga"          " 355" " 54" " 78"  "103"   " 53"     " 45"     
##  [670,] "Carracosta"        " 495" " 74" "108"  "133"   " 83"     " 65"     
##  [671,] "Archen"            " 401" " 55" "112"  " 45"   " 74"     " 45"     
##  [672,] "Archeops"          " 567" " 75" "140"  " 65"   "112"     " 65"     
##  [673,] "Trubbish"          " 329" " 50" " 50"  " 62"   " 40"     " 62"     
##  [674,] "Garbodor"          " 474" " 80" " 95"  " 82"   " 60"     " 82"     
##  [675,] "Zorua"             " 330" " 40" " 65"  " 40"   " 80"     " 40"     
##  [676,] "Zoroark"           " 510" " 60" "105"  " 60"   "120"     " 60"     
##  [677,] "Minccino"          " 300" " 55" " 50"  " 40"   " 40"     " 40"     
##  [678,] "Cinccino"          " 470" " 75" " 95"  " 60"   " 65"     " 60"     
##  [679,] "Gothita"           " 290" " 45" " 30"  " 50"   " 55"     " 65"     
##  [680,] "Gothorita"         " 390" " 60" " 45"  " 70"   " 75"     " 85"     
##  [681,] "Gothitelle"        " 490" " 70" " 55"  " 95"   " 95"     "110"     
##  [682,] "Solosis"           " 290" " 45" " 30"  " 40"   "105"     " 50"     
##  [683,] "Duosion"           " 370" " 65" " 40"  " 50"   "125"     " 60"     
##  [684,] "Reuniclus"         " 490" "110" " 65"  " 75"   "125"     " 85"     
##  [685,] "Ducklett"          " 305" " 62" " 44"  " 50"   " 44"     " 50"     
##  [686,] "Swanna"            " 473" " 75" " 87"  " 63"   " 87"     " 63"     
##  [687,] "Vanillite"         " 305" " 36" " 50"  " 50"   " 65"     " 60"     
##  [688,] "Vanillish"         " 395" " 51" " 65"  " 65"   " 80"     " 75"     
##  [689,] "Vanilluxe"         " 535" " 71" " 95"  " 85"   "110"     " 95"     
##  [690,] "Deerling"          " 335" " 60" " 60"  " 50"   " 40"     " 50"     
##  [691,] "Sawsbuck"          " 475" " 80" "100"  " 70"   " 60"     " 70"     
##  [692,] "Emolga"            " 428" " 55" " 75"  " 60"   " 75"     " 60"     
##  [693,] "Karrablast"        " 315" " 50" " 75"  " 45"   " 40"     " 45"     
##  [694,] "Escavalier"        " 495" " 70" "135"  "105"   " 60"     "105"     
##  [695,] "Foongus"           " 294" " 69" " 55"  " 45"   " 55"     " 55"     
##  [696,] "Amoonguss"         " 464" "114" " 85"  " 70"   " 85"     " 80"     
##  [697,] "Frillish"          " 335" " 55" " 40"  " 50"   " 65"     " 85"     
##  [698,] "Jellicent"         " 480" "100" " 60"  " 70"   " 85"     "105"     
##  [699,] "Alomomola"         " 470" "165" " 75"  " 80"   " 40"     " 45"     
##  [700,] "Joltik"            " 319" " 50" " 47"  " 50"   " 57"     " 50"     
##  [701,] "Galvantula"        " 472" " 70" " 77"  " 60"   " 97"     " 60"     
##  [702,] "Ferroseed"         " 305" " 44" " 50"  " 91"   " 24"     " 86"     
##  [703,] "Ferrothorn"        " 489" " 74" " 94"  "131"   " 54"     "116"     
##  [704,] "Klink"             " 300" " 40" " 55"  " 70"   " 45"     " 60"     
##  [705,] "Klang"             " 440" " 60" " 80"  " 95"   " 70"     " 85"     
##  [706,] "Klinklang"         " 520" " 60" "100"  "115"   " 70"     " 85"     
##  [707,] "Tynamo"            " 275" " 35" " 55"  " 40"   " 45"     " 40"     
##  [708,] "Eelektrik"         " 405" " 65" " 85"  " 70"   " 75"     " 70"     
##  [709,] "Eelektross"        " 515" " 85" "115"  " 80"   "105"     " 80"     
##  [710,] "Elgyem"            " 335" " 55" " 55"  " 55"   " 85"     " 55"     
##  [711,] "Beheeyem"          " 485" " 75" " 75"  " 75"   "125"     " 95"     
##  [712,] "Litwick"           " 275" " 50" " 30"  " 55"   " 65"     " 55"     
##  [713,] "Lampent"           " 370" " 60" " 40"  " 60"   " 95"     " 60"     
##  [714,] "Chandelure"        " 520" " 60" " 55"  " 90"   "145"     " 90"     
##  [715,] "Axew"              " 320" " 46" " 87"  " 60"   " 30"     " 40"     
##  [716,] "Fraxure"           " 410" " 66" "117"  " 70"   " 40"     " 50"     
##  [717,] "Haxorus"           " 540" " 76" "147"  " 90"   " 60"     " 70"     
##  [718,] "Cubchoo"           " 305" " 55" " 70"  " 40"   " 60"     " 40"     
##  [719,] "Beartic"           " 505" " 95" "130"  " 80"   " 70"     " 80"     
##  [720,] "Cryogonal"         " 515" " 80" " 50"  " 50"   " 95"     "135"     
##  [721,] "Shelmet"           " 305" " 50" " 40"  " 85"   " 40"     " 65"     
##  [722,] "Accelgor"          " 495" " 80" " 70"  " 40"   "100"     " 60"     
##  [723,] "Stunfisk"          " 471" "109" " 66"  " 84"   " 81"     " 99"     
##  [724,] "Mega Stunfisk"     " 471" "109" " 81"  " 99"   " 66"     " 84"     
##  [725,] "Mienfoo"           " 350" " 45" " 85"  " 50"   " 55"     " 50"     
##  [726,] "Mienshao"          " 510" " 65" "125"  " 60"   " 95"     " 60"     
##  [727,] "Druddigon"         " 485" " 77" "120"  " 90"   " 60"     " 90"     
##  [728,] "Golett"            " 303" " 59" " 74"  " 50"   " 35"     " 50"     
##  [729,] "Golurk"            " 483" " 89" "124"  " 80"   " 55"     " 80"     
##  [730,] "Pawniard"          " 340" " 45" " 85"  " 70"   " 40"     " 40"     
##  [731,] "Bisharp"           " 490" " 65" "125"  "100"   " 60"     " 70"     
##  [732,] "Bouffalant"        " 490" " 95" "110"  " 95"   " 40"     " 95"     
##  [733,] "Rufflet"           " 350" " 70" " 83"  " 50"   " 37"     " 50"     
##  [734,] "Braviary"          " 510" "100" "123"  " 75"   " 57"     " 75"     
##  [735,] "Vullaby"           " 370" " 70" " 55"  " 75"   " 45"     " 65"     
##  [736,] "Mandibuzz"         " 510" "110" " 65"  "105"   " 55"     " 95"     
##  [737,] "Heatmor"           " 484" " 85" " 97"  " 66"   "105"     " 66"     
##  [738,] "Durant"            " 484" " 58" "109"  "112"   " 48"     " 48"     
##  [739,] "Deino"             " 300" " 52" " 65"  " 50"   " 45"     " 50"     
##  [740,] "Zweilous"          " 420" " 72" " 85"  " 70"   " 65"     " 70"     
##  [741,] "Hydreigon"         " 600" " 92" "105"  " 90"   "125"     " 90"     
##  [742,] "Larvesta"          " 360" " 55" " 85"  " 55"   " 50"     " 55"     
##  [743,] "Volcarona"         " 550" " 85" " 60"  " 65"   "135"     "105"     
##  [744,] "Cobalion"          " 580" " 91" " 90"  "129"   " 90"     " 72"     
##  [745,] "Terrakion"         " 580" " 91" "129"  " 90"   " 72"     " 90"     
##  [746,] "Virizion"          " 580" " 91" " 90"  " 72"   " 90"     "129"     
##  [747,] "Tornadus"          " 580" " 79" "115"  " 70"   "125"     " 80"     
##  [748,] "Mega Tornadus"     " 580" " 79" "100"  " 80"   "110"     " 90"     
##  [749,] "Thundurus"         " 580" " 79" "115"  " 70"   "125"     " 80"     
##  [750,] "Mega Thundurus"    " 580" " 79" "105"  " 70"   "145"     " 80"     
##  [751,] "Reshiram"          " 680" "100" "120"  "100"   "150"     "120"     
##  [752,] "Zekrom"            " 680" "100" "150"  "120"   "120"     "100"     
##  [753,] "Landorus"          " 600" " 89" "125"  " 90"   "115"     " 80"     
##  [754,] "Mega Landorus"     " 600" " 89" "145"  " 90"   "105"     " 80"     
##  [755,] "Kyurem"            " 660" "125" "130"  " 90"   "130"     " 90"     
##  [756,] "Mega Kyurem"       " 700" "125" "170"  "100"   "120"     " 90"     
##  [757,] "Mega Kyurem X"     " 700" "125" "120"  " 90"   "170"     "100"     
##  [758,] "Keldeo"            " 580" " 91" " 72"  " 90"   "129"     " 90"     
##  [759,] "Mega Keldeo"       " 580" " 91" " 72"  " 90"   "129"     " 90"     
##  [760,] "Meloetta"          " 600" "100" " 77"  " 77"   "128"     "128"     
##  [761,] "Mega Meloetta"     " 600" "100" "128"  " 90"   " 77"     " 77"     
##  [762,] "Genesect"          " 600" " 71" "120"  " 95"   "120"     " 95"     
##  [763,] "Chespin"           " 313" " 56" " 61"  " 65"   " 48"     " 45"     
##  [764,] "Quilladin"         " 405" " 61" " 78"  " 95"   " 56"     " 58"     
##  [765,] "Chesnaught"        " 530" " 88" "107"  "122"   " 74"     " 75"     
##  [766,] "Fennekin"          " 307" " 40" " 45"  " 40"   " 62"     " 60"     
##  [767,] "Braixen"           " 409" " 59" " 59"  " 58"   " 90"     " 70"     
##  [768,] "Delphox"           " 534" " 75" " 69"  " 72"   "114"     "100"     
##  [769,] "Froakie"           " 314" " 41" " 56"  " 40"   " 62"     " 44"     
##  [770,] "Frogadier"         " 405" " 54" " 63"  " 52"   " 83"     " 56"     
##  [771,] "Greninja"          " 530" " 72" " 95"  " 67"   "103"     " 71"     
##  [772,] "Mega Greninja"     " 640" " 72" "145"  " 67"   "153"     " 71"     
##  [773,] "Bunnelby"          " 237" " 38" " 36"  " 38"   " 32"     " 36"     
##  [774,] "Diggersby"         " 423" " 85" " 56"  " 77"   " 50"     " 77"     
##  [775,] "Fletchling"        " 278" " 45" " 50"  " 43"   " 40"     " 38"     
##  [776,] "Fletchinder"       " 382" " 62" " 73"  " 55"   " 56"     " 52"     
##  [777,] "Talonflame"        " 499" " 78" " 81"  " 71"   " 74"     " 69"     
##  [778,] "Scatterbug"        " 200" " 38" " 35"  " 40"   " 27"     " 25"     
##  [779,] "Spewpa"            " 213" " 45" " 22"  " 60"   " 27"     " 30"     
##  [780,] "Vivillon"          " 411" " 80" " 52"  " 50"   " 90"     " 50"     
##  [781,] "Litleo"            " 369" " 62" " 50"  " 58"   " 73"     " 54"     
##  [782,] "Pyroar"            " 507" " 86" " 68"  " 72"   "109"     " 66"     
##  [783,] "Flabébé"         " 303" " 44" " 38"  " 39"   " 61"     " 79"     
##  [784,] "Floette"           " 371" " 54" " 45"  " 47"   " 75"     " 98"     
##  [785,] "Florges"           " 552" " 78" " 65"  " 68"   "112"     "154"     
##  [786,] "Skiddo"            " 350" " 66" " 65"  " 48"   " 62"     " 57"     
##  [787,] "Gogoat"            " 531" "123" "100"  " 62"   " 97"     " 81"     
##  [788,] "Pancham"           " 348" " 67" " 82"  " 62"   " 46"     " 48"     
##  [789,] "Pangoro"           " 495" " 95" "124"  " 78"   " 69"     " 71"     
##  [790,] "Furfrou"           " 472" " 75" " 80"  " 60"   " 65"     " 90"     
##  [791,] "Espurr"            " 355" " 62" " 48"  " 54"   " 63"     " 60"     
##  [792,] "Meowstic"          " 466" " 74" " 48"  " 76"   " 83"     " 81"     
##  [793,] "Mega Meowstic"     " 466" " 74" " 48"  " 76"   " 83"     " 81"     
##  [794,] "Honedge"           " 325" " 45" " 80"  "100"   " 35"     " 37"     
##  [795,] "Doublade"          " 448" " 59" "110"  "150"   " 45"     " 49"     
##  [796,] "Aegislash"         " 500" " 60" " 50"  "140"   " 50"     "140"     
##  [797,] "Mega Aegislash"    " 500" " 60" "140"  " 50"   "140"     " 50"     
##  [798,] "Spritzee"          " 341" " 78" " 52"  " 60"   " 63"     " 65"     
##  [799,] "Aromatisse"        " 462" "101" " 72"  " 72"   " 99"     " 89"     
##  [800,] "Swirlix"           " 341" " 62" " 48"  " 66"   " 59"     " 57"     
##  [801,] "Slurpuff"          " 480" " 82" " 80"  " 86"   " 85"     " 75"     
##  [802,] "Inkay"             " 288" " 53" " 54"  " 53"   " 37"     " 46"     
##  [803,] "Malamar"           " 482" " 86" " 92"  " 88"   " 68"     " 75"     
##  [804,] "Binacle"           " 306" " 42" " 52"  " 67"   " 39"     " 56"     
##  [805,] "Barbaracle"        " 500" " 72" "105"  "115"   " 54"     " 86"     
##  [806,] "Skrelp"            " 320" " 50" " 60"  " 60"   " 60"     " 60"     
##  [807,] "Dragalge"          " 494" " 65" " 75"  " 90"   " 97"     "123"     
##  [808,] "Clauncher"         " 330" " 50" " 53"  " 62"   " 58"     " 63"     
##  [809,] "Clawitzer"         " 500" " 71" " 73"  " 88"   "120"     " 89"     
##  [810,] "Helioptile"        " 289" " 44" " 38"  " 33"   " 61"     " 43"     
##  [811,] "Heliolisk"         " 481" " 62" " 55"  " 52"   "109"     " 94"     
##  [812,] "Tyrunt"            " 362" " 58" " 89"  " 77"   " 45"     " 45"     
##  [813,] "Tyrantrum"         " 521" " 82" "121"  "119"   " 69"     " 59"     
##  [814,] "Amaura"            " 362" " 77" " 59"  " 50"   " 67"     " 63"     
##  [815,] "Aurorus"           " 521" "123" " 77"  " 72"   " 99"     " 92"     
##  [816,] "Sylveon"           " 525" " 95" " 65"  " 65"   "110"     "130"     
##  [817,] "Hawlucha"          " 500" " 78" " 92"  " 75"   " 74"     " 63"     
##  [818,] "Dedenne"           " 431" " 67" " 58"  " 57"   " 81"     " 67"     
##  [819,] "Carbink"           " 500" " 50" " 50"  "150"   " 50"     "150"     
##  [820,] "Goomy"             " 300" " 45" " 50"  " 35"   " 55"     " 75"     
##  [821,] "Sliggoo"           " 452" " 68" " 75"  " 53"   " 83"     "113"     
##  [822,] "Goodra"            " 600" " 90" "100"  " 70"   "110"     "150"     
##  [823,] "Klefki"            " 470" " 57" " 80"  " 91"   " 80"     " 87"     
##  [824,] "Phantump"          " 309" " 43" " 70"  " 48"   " 50"     " 60"     
##  [825,] "Trevenant"         " 474" " 85" "110"  " 76"   " 65"     " 82"     
##  [826,] "Pumpkaboo"         " 335" " 49" " 66"  " 70"   " 44"     " 55"     
##  [827,] "Mega Pumpkaboo"    " 335" " 44" " 66"  " 70"   " 44"     " 55"     
##  [828,] "Mega Pumpkaboo X"  " 335" " 54" " 66"  " 70"   " 44"     " 55"     
##  [829,] "Mega Pumpkaboo X"  " 335" " 59" " 66"  " 70"   " 44"     " 55"     
##  [830,] "Gourgeist"         " 494" " 65" " 90"  "122"   " 58"     " 75"     
##  [831,] "Mega Gourgeist"    " 494" " 55" " 85"  "122"   " 58"     " 75"     
##  [832,] "Mega Gourgeist X"  " 494" " 75" " 95"  "122"   " 58"     " 75"     
##  [833,] "Mega Gourgeist X"  " 494" " 85" "100"  "122"   " 58"     " 75"     
##  [834,] "Bergmite"          " 304" " 55" " 69"  " 85"   " 32"     " 35"     
##  [835,] "Avalugg"           " 514" " 95" "117"  "184"   " 44"     " 46"     
##  [836,] "Noibat"            " 245" " 40" " 30"  " 35"   " 45"     " 40"     
##  [837,] "Noivern"           " 535" " 85" " 70"  " 80"   " 97"     " 80"     
##  [838,] "Xerneas"           " 680" "126" "131"  " 95"   "131"     " 98"     
##  [839,] "Yveltal"           " 680" "126" "131"  " 95"   "131"     " 98"     
##  [840,] "Zygarde"           " 600" "108" "100"  "121"   " 81"     " 95"     
##  [841,] "Mega Zygarde"      " 486" " 54" "100"  " 71"   " 61"     " 85"     
##  [842,] "Mega Zygarde X"    " 708" "216" "100"  "121"   " 91"     " 95"     
##  [843,] "Diancie"           " 600" " 50" "100"  "150"   "100"     "150"     
##  [844,] "Mega Diancie"      " 700" " 50" "160"  "110"   "160"     "110"     
##  [845,] "Hoopa"             " 600" " 80" "110"  " 60"   "150"     "130"     
##  [846,] "Mega Hoopa"        " 680" " 80" "160"  " 60"   "170"     "130"     
##  [847,] "Volcanion"         " 600" " 80" "110"  "120"   "130"     " 90"     
##  [848,] "Rowlet"            " 320" " 68" " 55"  " 55"   " 50"     " 50"     
##  [849,] "Dartrix"           " 420" " 78" " 75"  " 75"   " 70"     " 70"     
##  [850,] "Decidueye"         " 530" " 78" "107"  " 75"   "100"     "100"     
##  [851,] "Litten"            " 320" " 45" " 65"  " 40"   " 60"     " 40"     
##  [852,] "Torracat"          " 420" " 65" " 85"  " 50"   " 80"     " 50"     
##  [853,] "Incineroar"        " 530" " 95" "115"  " 90"   " 80"     " 90"     
##  [854,] "Popplio"           " 320" " 50" " 54"  " 54"   " 66"     " 56"     
##  [855,] "Brionne"           " 420" " 60" " 69"  " 69"   " 91"     " 81"     
##  [856,] "Primarina"         " 530" " 80" " 74"  " 74"   "126"     "116"     
##  [857,] "Pikipek"           " 265" " 35" " 75"  " 30"   " 30"     " 30"     
##  [858,] "Trumbeak"          " 355" " 55" " 85"  " 50"   " 40"     " 50"     
##  [859,] "Toucannon"         " 485" " 80" "120"  " 75"   " 75"     " 75"     
##  [860,] "Yungoos"           " 253" " 48" " 70"  " 30"   " 30"     " 30"     
##  [861,] "Gumshoos"          " 418" " 88" "110"  " 60"   " 55"     " 60"     
##  [862,] "Grubbin"           " 300" " 47" " 62"  " 45"   " 55"     " 45"     
##  [863,] "Charjabug"         " 400" " 57" " 82"  " 95"   " 55"     " 75"     
##  [864,] "Vikavolt"          " 500" " 77" " 70"  " 90"   "145"     " 75"     
##  [865,] "Crabrawler"        " 338" " 47" " 82"  " 57"   " 42"     " 47"     
##  [866,] "Crabominable"      " 478" " 97" "132"  " 77"   " 62"     " 67"     
##  [867,] "Oricorio"          " 476" " 75" " 70"  " 70"   " 98"     " 70"     
##  [868,] "Mega Oricorio"     " 476" " 75" " 70"  " 70"   " 98"     " 70"     
##  [869,] "Mega Oricorio X"   " 476" " 75" " 70"  " 70"   " 98"     " 70"     
##  [870,] "Mega Oricorio X"   " 476" " 75" " 70"  " 70"   " 98"     " 70"     
##  [871,] "Cutiefly"          " 304" " 40" " 45"  " 40"   " 55"     " 40"     
##  [872,] "Ribombee"          " 464" " 60" " 55"  " 60"   " 95"     " 70"     
##  [873,] "Rockruff"          " 280" " 45" " 65"  " 40"   " 30"     " 40"     
##  [874,] "Mega Rockruff"     " 280" " 45" " 65"  " 40"   " 30"     " 40"     
##  [875,] "Lycanroc"          " 487" " 75" "115"  " 65"   " 55"     " 65"     
##  [876,] "Mega Lycanroc"     " 487" " 85" "115"  " 75"   " 55"     " 75"     
##  [877,] "Mega Lycanroc X"   " 487" " 75" "117"  " 65"   " 55"     " 65"     
##  [878,] "Wishiwashi"        " 175" " 45" " 20"  " 20"   " 25"     " 25"     
##  [879,] "Mega Wishiwashi"   " 620" " 45" "140"  "130"   "140"     "135"     
##  [880,] "Mareanie"          " 305" " 50" " 53"  " 62"   " 43"     " 52"     
##  [881,] "Toxapex"           " 495" " 50" " 63"  "152"   " 53"     "142"     
##  [882,] "Mudbray"           " 385" " 70" "100"  " 70"   " 45"     " 55"     
##  [883,] "Mudsdale"          " 500" "100" "125"  "100"   " 55"     " 85"     
##  [884,] "Dewpider"          " 269" " 38" " 40"  " 52"   " 40"     " 72"     
##  [885,] "Araquanid"         " 454" " 68" " 70"  " 92"   " 50"     "132"     
##  [886,] "Fomantis"          " 250" " 40" " 55"  " 35"   " 50"     " 35"     
##  [887,] "Lurantis"          " 480" " 70" "105"  " 90"   " 80"     " 90"     
##  [888,] "Morelull"          " 285" " 40" " 35"  " 55"   " 65"     " 75"     
##  [889,] "Shiinotic"         " 405" " 60" " 45"  " 80"   " 90"     "100"     
##  [890,] "Salandit"          " 320" " 48" " 44"  " 40"   " 71"     " 40"     
##  [891,] "Salazzle"          " 480" " 68" " 64"  " 60"   "111"     " 60"     
##  [892,] "Stufful"           " 340" " 70" " 75"  " 50"   " 45"     " 50"     
##  [893,] "Bewear"            " 500" "120" "125"  " 80"   " 55"     " 60"     
##  [894,] "Bounsweet"         " 210" " 42" " 30"  " 38"   " 30"     " 38"     
##  [895,] "Steenee"           " 290" " 52" " 40"  " 48"   " 40"     " 48"     
##  [896,] "Tsareena"          " 510" " 72" "120"  " 98"   " 50"     " 98"     
##  [897,] "Comfey"            " 485" " 51" " 52"  " 90"   " 82"     "110"     
##  [898,] "Oranguru"          " 490" " 90" " 60"  " 80"   " 90"     "110"     
##  [899,] "Passimian"         " 490" "100" "120"  " 90"   " 40"     " 60"     
##  [900,] "Wimpod"            " 230" " 25" " 35"  " 40"   " 20"     " 30"     
##  [901,] "Golisopod"         " 530" " 75" "125"  "140"   " 60"     " 90"     
##  [902,] "Sandygast"         " 320" " 55" " 55"  " 80"   " 70"     " 45"     
##  [903,] "Palossand"         " 480" " 85" " 75"  "110"   "100"     " 75"     
##  [904,] "Pyukumuku"         " 410" " 55" " 60"  "130"   " 30"     "130"     
##  [905,] "Type: Null"        " 534" " 95" " 95"  " 95"   " 95"     " 95"     
##  [906,] "Silvally"          " 570" " 95" " 95"  " 95"   " 95"     " 95"     
##  [907,] "Minior"            " 440" " 60" " 60"  "100"   " 60"     "100"     
##  [908,] "Mega Minior"       " 500" " 60" "100"  " 60"   "100"     " 60"     
##  [909,] "Komala"            " 480" " 65" "115"  " 65"   " 75"     " 95"     
##  [910,] "Turtonator"        " 485" " 60" " 78"  "135"   " 91"     " 85"     
##  [911,] "Togedemaru"        " 435" " 65" " 98"  " 63"   " 40"     " 73"     
##  [912,] "Mimikyu"           " 476" " 55" " 90"  " 80"   " 50"     "105"     
##  [913,] "Bruxish"           " 475" " 68" "105"  " 70"   " 70"     " 70"     
##  [914,] "Drampa"            " 485" " 78" " 60"  " 85"   "135"     " 91"     
##  [915,] "Dhelmise"          " 517" " 70" "131"  "100"   " 86"     " 90"     
##  [916,] "Jangmo-o"          " 300" " 45" " 55"  " 65"   " 45"     " 45"     
##  [917,] "Hakamo-o"          " 420" " 55" " 75"  " 90"   " 65"     " 70"     
##  [918,] "Kommo-o"           " 600" " 75" "110"  "125"   "100"     "105"     
##  [919,] "Tapu Koko"         " 570" " 70" "115"  " 85"   " 95"     " 75"     
##  [920,] "Tapu Lele"         " 570" " 70" " 85"  " 75"   "130"     "115"     
##  [921,] "Tapu Bulu"         " 570" " 70" "130"  "115"   " 85"     " 95"     
##  [922,] "Tapu Fini"         " 570" " 70" " 75"  "115"   " 95"     "130"     
##  [923,] "Cosmog"            " 200" " 43" " 29"  " 31"   " 29"     " 31"     
##  [924,] "Cosmoem"           " 400" " 43" " 29"  "131"   " 29"     "131"     
##  [925,] "Solgaleo"          " 680" "137" "137"  "107"   "113"     " 89"     
##  [926,] "Lunala"            " 680" "137" "113"  " 89"   "137"     "107"     
##  [927,] "Nihilego"          " 570" "109" " 53"  " 47"   "127"     "131"     
##  [928,] "Buzzwole"          " 570" "107" "139"  "139"   " 53"     " 53"     
##  [929,] "Pheromosa"         " 570" " 71" "137"  " 37"   "137"     " 37"     
##  [930,] "Xurkitree"         " 570" " 83" " 89"  " 71"   "173"     " 71"     
##  [931,] "Celesteela"        " 570" " 97" "101"  "103"   "107"     "101"     
##  [932,] "Kartana"           " 570" " 59" "181"  "131"   " 59"     " 31"     
##  [933,] "Guzzlord"          " 570" "223" "101"  " 53"   " 97"     " 53"     
##  [934,] "Necrozma"          " 600" " 97" "107"  "101"   "127"     " 89"     
##  [935,] "Mega Necrozma"     " 680" " 97" "157"  "127"   "113"     "109"     
##  [936,] "Mega Necrozma X"   " 680" " 97" "113"  "109"   "157"     "127"     
##  [937,] "Mega Necrozma X"   " 754" " 97" "167"  " 97"   "167"     " 97"     
##  [938,] "Magearna"          " 600" " 80" " 95"  "115"   "130"     "115"     
##  [939,] "Marshadow"         " 600" " 90" "125"  " 80"   " 90"     " 90"     
##  [940,] "Poipole"           " 420" " 67" " 73"  " 67"   " 73"     " 67"     
##  [941,] "Naganadel"         " 540" " 73" " 73"  " 73"   "127"     " 73"     
##  [942,] "Stakataka"         " 570" " 61" "131"  "211"   " 53"     "101"     
##  [943,] "Blacephalon"       " 570" " 53" "127"  " 53"   "151"     " 79"     
##  [944,] "Zeraora"           " 600" " 88" "112"  " 75"   "102"     " 80"     
##  [945,] "Meltan"            " 300" " 46" " 65"  " 65"   " 55"     " 35"     
##  [946,] "Melmetal"          " 600" "135" "143"  "143"   " 80"     " 65"     
##  [947,] "Grookey"           " 310" " 50" " 65"  " 50"   " 40"     " 40"     
##  [948,] "Thwackey"          " 420" " 70" " 85"  " 70"   " 55"     " 60"     
##  [949,] "Rillaboom"         " 530" "100" "125"  " 90"   " 60"     " 70"     
##  [950,] "Scorbunny"         " 310" " 50" " 71"  " 40"   " 40"     " 40"     
##  [951,] "Raboot"            " 420" " 65" " 86"  " 60"   " 55"     " 60"     
##  [952,] "Cinderace"         " 530" " 80" "116"  " 75"   " 65"     " 75"     
##  [953,] "Sobble"            " 310" " 50" " 40"  " 40"   " 70"     " 40"     
##  [954,] "Drizzile"          " 420" " 65" " 60"  " 55"   " 95"     " 55"     
##  [955,] "Inteleon"          " 530" " 70" " 85"  " 65"   "125"     " 65"     
##  [956,] "Skwovet"           " 275" " 70" " 55"  " 55"   " 35"     " 35"     
##  [957,] "Greedent"          " 460" "120" " 95"  " 95"   " 55"     " 75"     
##  [958,] "Rookidee"          " 245" " 38" " 47"  " 35"   " 33"     " 35"     
##  [959,] "Corvisquire"       " 365" " 68" " 67"  " 55"   " 43"     " 55"     
##  [960,] "Corviknight"       " 495" " 98" " 87"  "105"   " 53"     " 85"     
##  [961,] "Blipbug"           " 180" " 25" " 20"  " 20"   " 25"     " 45"     
##  [962,] "Dottler"           " 335" " 50" " 35"  " 80"   " 50"     " 90"     
##  [963,] "Orbeetle"          " 505" " 60" " 45"  "110"   " 80"     "120"     
##  [964,] "Nickit"            " 245" " 40" " 28"  " 28"   " 47"     " 52"     
##  [965,] "Thievul"           " 455" " 70" " 58"  " 58"   " 87"     " 92"     
##  [966,] "Gossifleur"        " 250" " 40" " 40"  " 60"   " 40"     " 60"     
##  [967,] "Eldegoss"          " 460" " 60" " 50"  " 90"   " 80"     "120"     
##  [968,] "Wooloo"            " 270" " 42" " 40"  " 55"   " 40"     " 45"     
##  [969,] "Dubwool"           " 490" " 72" " 80"  "100"   " 60"     " 90"     
##  [970,] "Chewtle"           " 284" " 50" " 64"  " 50"   " 38"     " 38"     
##  [971,] "Drednaw"           " 485" " 90" "115"  " 90"   " 48"     " 68"     
##  [972,] "Yamper"            " 270" " 59" " 45"  " 50"   " 40"     " 50"     
##  [973,] "Boltund"           " 490" " 69" " 90"  " 60"   " 90"     " 60"     
##  [974,] "Rolycoly"          " 240" " 30" " 40"  " 50"   " 40"     " 50"     
##  [975,] "Carkol"            " 410" " 80" " 60"  " 90"   " 60"     " 70"     
##  [976,] "Coalossal"         " 510" "110" " 80"  "120"   " 80"     " 90"     
##  [977,] "Applin"            " 260" " 40" " 40"  " 80"   " 40"     " 40"     
##  [978,] "Flapple"           " 485" " 70" "110"  " 80"   " 95"     " 60"     
##  [979,] "Appletun"          " 485" "110" " 85"  " 80"   "100"     " 80"     
##  [980,] "Silicobra"         " 315" " 52" " 57"  " 75"   " 35"     " 50"     
##  [981,] "Sandaconda"        " 510" " 72" "107"  "125"   " 65"     " 70"     
##  [982,] "Cramorant"         " 475" " 70" " 85"  " 55"   " 85"     " 95"     
##  [983,] "Arrokuda"          " 280" " 41" " 63"  " 40"   " 40"     " 30"     
##  [984,] "Barraskewda"       " 490" " 61" "123"  " 60"   " 60"     " 50"     
##  [985,] "Toxel"             " 242" " 40" " 38"  " 35"   " 54"     " 35"     
##  [986,] "Toxtricity"        " 502" " 75" " 98"  " 70"   "114"     " 70"     
##  [987,] "Mega Toxtricity"   " 502" " 75" " 98"  " 70"   "114"     " 70"     
##  [988,] "Sizzlipede"        " 305" " 50" " 65"  " 45"   " 50"     " 50"     
##  [989,] "Centiskorch"       " 525" "100" "115"  " 65"   " 90"     " 90"     
##  [990,] "Clobbopus"         " 310" " 50" " 68"  " 60"   " 50"     " 50"     
##  [991,] "Grapploct"         " 480" " 80" "118"  " 90"   " 70"     " 80"     
##  [992,] "Sinistea"          " 308" " 40" " 45"  " 45"   " 74"     " 54"     
##  [993,] "Polteageist"       " 508" " 60" " 65"  " 65"   "134"     "114"     
##  [994,] "Hatenna"           " 265" " 42" " 30"  " 45"   " 56"     " 53"     
##  [995,] "Hattrem"           " 370" " 57" " 40"  " 65"   " 86"     " 73"     
##  [996,] "Hatterene"         " 510" " 57" " 90"  " 95"   "136"     "103"     
##  [997,] "Impidimp"          " 265" " 45" " 45"  " 30"   " 55"     " 40"     
##  [998,] "Morgrem"           " 370" " 65" " 60"  " 45"   " 75"     " 55"     
##  [999,] "Grimmsnarl"        " 510" " 95" "120"  " 65"   " 95"     " 75"     
## [1000,] "Obstagoon"         " 520" " 93" " 90"  "101"   " 60"     " 81"     
## [1001,] "Perrserker"        " 440" " 70" "110"  "100"   " 50"     " 60"     
## [1002,] "Cursola"           " 510" " 60" " 95"  " 50"   "145"     "130"     
## [1003,] "Sirfetch'd"        " 507" " 62" "135"  " 95"   " 68"     " 82"     
## [1004,] "Mr. Rime"          " 520" " 80" " 85"  " 75"   "110"     "100"     
## [1005,] "Runerigus"         " 483" " 58" " 95"  "145"   " 50"     "105"     
## [1006,] "Milcery"           " 270" " 45" " 40"  " 40"   " 50"     " 61"     
## [1007,] "Alcremie"          " 495" " 65" " 60"  " 75"   "110"     "121"     
## [1008,] "Falinks"           " 470" " 65" "100"  "100"   " 70"     " 60"     
## [1009,] "Pincurchin"        " 435" " 48" "101"  " 95"   " 91"     " 85"     
## [1010,] "Snom"              " 185" " 30" " 25"  " 35"   " 45"     " 30"     
## [1011,] "Frosmoth"          " 475" " 70" " 65"  " 60"   "125"     " 90"     
## [1012,] "Stonjourner"       " 470" "100" "125"  "135"   " 20"     " 20"     
## [1013,] "Eiscue"            " 470" " 75" " 80"  "110"   " 65"     " 90"     
## [1014,] "Mega Eiscue"       " 470" " 75" " 80"  " 70"   " 65"     " 50"     
## [1015,] "Indeedee"          " 475" " 60" " 65"  " 55"   "105"     " 95"     
## [1016,] "Mega Indeedee"     " 475" " 70" " 55"  " 65"   " 95"     "105"     
## [1017,] "Morpeko"           " 436" " 58" " 95"  " 58"   " 70"     " 58"     
## [1018,] "Mega Morpeko"      " 436" " 58" " 95"  " 58"   " 70"     " 58"     
## [1019,] "Cufant"            " 330" " 72" " 80"  " 49"   " 40"     " 49"     
## [1020,] "Copperajah"        " 500" "122" "130"  " 69"   " 80"     " 69"     
## [1021,] "Dracozolt"         " 505" " 90" "100"  " 90"   " 80"     " 70"     
## [1022,] "Arctozolt"         " 505" " 90" "100"  " 90"   " 90"     " 80"     
## [1023,] "Dracovish"         " 505" " 90" " 90"  "100"   " 70"     " 80"     
## [1024,] "Arctovish"         " 505" " 90" " 90"  "100"   " 80"     " 90"     
## [1025,] "Duraludon"         " 535" " 70" " 95"  "115"   "120"     " 50"     
## [1026,] "Dreepy"            " 270" " 28" " 60"  " 30"   " 40"     " 30"     
## [1027,] "Drakloak"          " 410" " 68" " 80"  " 50"   " 60"     " 50"     
## [1028,] "Dragapult"         " 600" " 88" "120"  " 75"   "100"     " 75"     
## [1029,] "Zacian"            " 720" " 92" "170"  "115"   " 80"     "115"     
## [1030,] "Mega Zacian"       " 670" " 92" "130"  "115"   " 80"     "115"     
## [1031,] "Zamazenta"         " 720" " 92" "130"  "145"   " 80"     "145"     
## [1032,] "Mega Zamazenta"    " 670" " 92" "130"  "115"   " 80"     "115"     
## [1033,] "Eternatus"         " 690" "140" " 85"  " 95"   "145"     " 95"     
## [1034,] "Mega Eternatus"    "1125" "255" "115"  "250"   "125"     "250"     
## [1035,] "Kubfu"             " 385" " 60" " 90"  " 60"   " 53"     " 50"     
## [1036,] "Urshifu"           " 550" "100" "130"  "100"   " 63"     " 60"     
## [1037,] "Mega Urshifu"      " 550" "100" "130"  "100"   " 63"     " 60"     
## [1038,] "Zarude"            " 600" "105" "120"  "105"   " 70"     " 95"     
## [1039,] "Regieleki"         " 580" " 80" "100"  " 50"   "100"     " 50"     
## [1040,] "Regidrago"         " 580" "200" "100"  " 50"   "100"     " 50"     
## [1041,] "Glastrier"         " 580" "100" "145"  "130"   " 65"     "110"     
## [1042,] "Spectrier"         " 580" "100" " 65"  " 60"   "145"     " 80"     
## [1043,] "Calyrex"           " 500" "100" " 80"  " 80"   " 80"     " 80"     
## [1044,] "Mega Calyrex"      " 680" "100" "165"  "150"   " 85"     "130"     
## [1045,] "Mega Calyrex X"    " 680" "100" " 85"  " 80"   "165"     "100"     
##         Speed
##    [1,] " 45"
##    [2,] " 60"
##    [3,] " 80"
##    [4,] " 80"
##    [5,] " 65"
##    [6,] " 80"
##    [7,] "100"
##    [8,] "100"
##    [9,] "100"
##   [10,] " 43"
##   [11,] " 58"
##   [12,] " 78"
##   [13,] " 78"
##   [14,] " 45"
##   [15,] " 30"
##   [16,] " 70"
##   [17,] " 50"
##   [18,] " 35"
##   [19,] " 75"
##   [20,] "145"
##   [21,] " 56"
##   [22,] " 71"
##   [23,] "101"
##   [24,] "121"
##   [25,] " 72"
##   [26,] " 72"
##   [27,] " 97"
##   [28,] " 77"
##   [29,] " 70"
##   [30,] "100"
##   [31,] " 55"
##   [32,] " 80"
##   [33,] " 90"
##   [34,] "120"
##   [35,] "110"
##   [36,] "110"
##   [37,] " 40"
##   [38,] " 40"
##   [39,] " 65"
##   [40,] " 65"
##   [41,] " 41"
##   [42,] " 56"
##   [43,] " 76"
##   [44,] " 50"
##   [45,] " 65"
##   [46,] " 85"
##   [47,] " 35"
##   [48,] " 60"
##   [49,] " 65"
##   [50,] " 65"
##   [51,] "100"
##   [52,] "109"
##   [53,] " 20"
##   [54,] " 45"
##   [55,] " 55"
##   [56,] " 90"
##   [57,] " 30"
##   [58,] " 40"
##   [59,] " 50"
##   [60,] " 25"
##   [61,] " 30"
##   [62,] " 45"
##   [63,] " 90"
##   [64,] " 95"
##   [65,] " 90"
##   [66,] "120"
##   [67,] "110"
##   [68,] " 90"
##   [69,] " 90"
##   [70,] " 40"
##   [71,] "115"
##   [72,] "115"
##   [73,] " 55"
##   [74,] " 85"
##   [75,] " 70"
##   [76,] " 95"
##   [77,] " 60"
##   [78,] " 95"
##   [79,] " 90"
##   [80,] " 90"
##   [81,] " 70"
##   [82,] " 90"
##   [83,] "105"
##   [84,] "120"
##   [85,] "150"
##   [86,] " 35"
##   [87,] " 45"
##   [88,] " 55"
##   [89,] " 40"
##   [90,] " 55"
##   [91,] " 70"
##   [92,] " 70"
##   [93,] "100"
##   [94,] " 20"
##   [95,] " 20"
##   [96,] " 35"
##   [97,] " 35"
##   [98,] " 45"
##   [99,] " 45"
##  [100,] " 90"
##  [101,] " 90"
##  [102,] "105"
##  [103,] "105"
##  [104,] " 15"
##  [105,] " 15"
##  [106,] " 30"
##  [107,] " 30"
##  [108,] " 30"
##  [109,] " 45"
##  [110,] " 70"
##  [111,] " 60"
##  [112,] " 55"
##  [113,] " 75"
##  [114,] "110"
##  [115,] " 45"
##  [116,] " 70"
##  [117,] " 25"
##  [118,] " 25"
##  [119,] " 50"
##  [120,] " 50"
##  [121,] " 40"
##  [122,] " 70"
##  [123,] " 80"
##  [124,] " 95"
##  [125,] "110"
##  [126,] "130"
##  [127,] " 70"
##  [128,] " 42"
##  [129,] " 67"
##  [130,] " 50"
##  [131,] " 75"
##  [132,] "100"
##  [133,] "150"
##  [134,] " 40"
##  [135,] " 55"
##  [136,] " 45"
##  [137,] " 35"
##  [138,] " 45"
##  [139,] " 45"
##  [140,] " 87"
##  [141,] " 76"
##  [142,] " 30"
##  [143,] " 35"
##  [144,] " 60"
##  [145,] " 60"
##  [146,] " 25"
##  [147,] " 40"
##  [148,] " 50"
##  [149,] " 60"
##  [150,] " 90"
##  [151,] "100"
##  [152,] " 60"
##  [153,] " 85"
##  [154,] " 63"
##  [155,] " 68"
##  [156,] " 85"
##  [157,] "115"
##  [158,] " 90"
##  [159,] "100"
##  [160,] "105"
##  [161,] " 95"
##  [162,] "105"
##  [163,] " 93"
##  [164,] " 85"
##  [165,] "105"
##  [166,] "110"
##  [167,] " 80"
##  [168,] " 81"
##  [169,] " 81"
##  [170,] " 60"
##  [171,] " 48"
##  [172,] " 55"
##  [173,] " 75"
##  [174,] " 65"
##  [175,] "130"
##  [176,] " 65"
##  [177,] " 40"
##  [178,] " 35"
##  [179,] " 55"
##  [180,] " 55"
##  [181,] " 80"
##  [182,] "130"
##  [183,] "150"
##  [184,] " 30"
##  [185,] " 85"
##  [186,] " 95"
##  [187,] "100"
##  [188,] "100"
##  [189,] " 90"
##  [190,] " 90"
##  [191,] " 50"
##  [192,] " 70"
##  [193,] " 80"
##  [194,] "130"
##  [195,] "130"
##  [196,] "140"
##  [197,] "100"
##  [198,] " 45"
##  [199,] " 60"
##  [200,] " 80"
##  [201,] " 65"
##  [202,] " 80"
##  [203,] "100"
##  [204,] " 43"
##  [205,] " 58"
##  [206,] " 78"
##  [207,] " 20"
##  [208,] " 90"
##  [209,] " 50"
##  [210,] " 70"
##  [211,] " 55"
##  [212,] " 85"
##  [213,] " 30"
##  [214,] " 40"
##  [215,] "130"
##  [216,] " 67"
##  [217,] " 67"
##  [218,] " 60"
##  [219,] " 15"
##  [220,] " 15"
##  [221,] " 20"
##  [222,] " 40"
##  [223,] " 70"
##  [224,] " 95"
##  [225,] " 35"
##  [226,] " 45"
##  [227,] " 55"
##  [228,] " 45"
##  [229,] " 50"
##  [230,] " 40"
##  [231,] " 50"
##  [232,] " 30"
##  [233,] " 70"
##  [234,] " 50"
##  [235,] " 80"
##  [236,] "110"
##  [237,] " 85"
##  [238,] " 30"
##  [239,] " 30"
##  [240,] " 95"
##  [241,] " 15"
##  [242,] " 35"
##  [243,] "110"
##  [244,] " 65"
##  [245,] " 91"
##  [246,] " 30"
##  [247,] " 30"
##  [248,] " 85"
##  [249,] " 48"
##  [250,] " 33"
##  [251,] " 85"
##  [252,] " 15"
##  [253,] " 40"
##  [254,] " 45"
##  [255,] " 85"
##  [256,] " 30"
##  [257,] " 30"
##  [258,] " 30"
##  [259,] " 45"
##  [260,] " 85"
##  [261,] " 65"
##  [262,] " 75"
##  [263,] "  5"
##  [264,] " 85"
##  [265,] " 75"
##  [266,] "115"
##  [267,] " 40"
##  [268,] " 55"
##  [269,] " 20"
##  [270,] " 30"
##  [271,] " 50"
##  [272,] " 50"
##  [273,] " 35"
##  [274,] " 30"
##  [275,] " 65"
##  [276,] " 45"
##  [277,] " 75"
##  [278,] " 70"
##  [279,] " 70"
##  [280,] " 65"
##  [281,] " 95"
##  [282,] "115"
##  [283,] " 85"
##  [284,] " 40"
##  [285,] " 50"
##  [286,] " 60"
##  [287,] " 85"
##  [288,] " 75"
##  [289,] " 35"
##  [290,] " 70"
##  [291,] " 65"
##  [292,] " 95"
##  [293,] " 83"
##  [294,] "100"
##  [295,] " 55"
##  [296,] "115"
##  [297,] "100"
##  [298,] " 85"
##  [299,] " 41"
##  [300,] " 51"
##  [301,] " 61"
##  [302,] " 71"
##  [303,] "110"
##  [304,] " 90"
##  [305,] "100"
##  [306,] " 70"
##  [307,] " 95"
##  [308,] "120"
##  [309,] "145"
##  [310,] " 45"
##  [311,] " 55"
##  [312,] " 80"
##  [313,] "100"
##  [314,] " 40"
##  [315,] " 50"
##  [316,] " 60"
##  [317,] " 70"
##  [318,] " 35"
##  [319,] " 70"
##  [320,] " 60"
##  [321,] " 60"
##  [322,] "100"
##  [323,] "100"
##  [324,] " 20"
##  [325,] " 15"
##  [326,] " 65"
##  [327,] " 15"
##  [328,] " 65"
##  [329,] " 30"
##  [330,] " 50"
##  [331,] " 70"
##  [332,] " 30"
##  [333,] " 60"
##  [334,] " 80"
##  [335,] " 85"
##  [336,] "125"
##  [337,] " 85"
##  [338,] " 65"
##  [339,] " 40"
##  [340,] " 50"
##  [341,] " 80"
##  [342,] "100"
##  [343,] " 65"
##  [344,] " 80"
##  [345,] " 35"
##  [346,] " 70"
##  [347,] " 30"
##  [348,] " 90"
##  [349,] "100"
##  [350,] " 40"
##  [351,] "160"
##  [352,] " 40"
##  [353,] " 28"
##  [354,] " 48"
##  [355,] " 68"
##  [356,] " 25"
##  [357,] " 50"
##  [358,] " 20"
##  [359,] " 30"
##  [360,] " 50"
##  [361,] " 90"
##  [362,] " 50"
##  [363,] " 20"
##  [364,] " 50"
##  [365,] " 50"
##  [366,] " 30"
##  [367,] " 40"
##  [368,] " 50"
##  [369,] " 50"
##  [370,] " 60"
##  [371,] " 80"
##  [372,] "100"
##  [373,] " 65"
##  [374,] "105"
##  [375,] "135"
##  [376,] " 95"
##  [377,] " 95"
##  [378,] " 85"
##  [379,] " 85"
##  [380,] " 65"
##  [381,] " 40"
##  [382,] " 55"
##  [383,] " 65"
##  [384,] " 95"
##  [385,] "105"
##  [386,] " 60"
##  [387,] " 60"
##  [388,] " 35"
##  [389,] " 40"
##  [390,] " 20"
##  [391,] " 20"
##  [392,] " 60"
##  [393,] " 80"
##  [394,] " 60"
##  [395,] " 10"
##  [396,] " 70"
##  [397,] "100"
##  [398,] " 35"
##  [399,] " 55"
##  [400,] " 50"
##  [401,] " 80"
##  [402,] " 80"
##  [403,] " 90"
##  [404,] " 65"
##  [405,] " 70"
##  [406,] " 70"
##  [407,] " 60"
##  [408,] " 60"
##  [409,] " 35"
##  [410,] " 55"
##  [411,] " 55"
##  [412,] " 75"
##  [413,] " 23"
##  [414,] " 43"
##  [415,] " 75"
##  [416,] " 45"
##  [417,] " 80"
##  [418,] " 81"
##  [419,] " 70"
##  [420,] " 70"
##  [421,] " 70"
##  [422,] " 70"
##  [423,] " 40"
##  [424,] " 45"
##  [425,] " 65"
##  [426,] " 75"
##  [427,] " 25"
##  [428,] " 25"
##  [429,] " 51"
##  [430,] " 65"
##  [431,] " 75"
##  [432,] "115"
##  [433,] " 23"
##  [434,] " 50"
##  [435,] " 80"
##  [436,] "100"
##  [437,] " 25"
##  [438,] " 45"
##  [439,] " 65"
##  [440,] " 32"
##  [441,] " 52"
##  [442,] " 52"
##  [443,] " 55"
##  [444,] " 97"
##  [445,] " 50"
##  [446,] " 50"
##  [447,] "100"
##  [448,] "120"
##  [449,] " 30"
##  [450,] " 50"
##  [451,] " 70"
##  [452,] "110"
##  [453,] " 50"
##  [454,] " 50"
##  [455,] " 50"
##  [456,] "110"
##  [457,] "110"
##  [458,] "110"
##  [459,] "110"
##  [460,] " 90"
##  [461,] " 90"
##  [462,] " 90"
##  [463,] " 90"
##  [464,] " 95"
##  [465,] "115"
##  [466,] "100"
##  [467,] "150"
##  [468,] "150"
##  [469,] " 90"
##  [470,] "180"
##  [471,] " 31"
##  [472,] " 36"
##  [473,] " 56"
##  [474,] " 61"
##  [475,] " 81"
##  [476,] "108"
##  [477,] " 40"
##  [478,] " 50"
##  [479,] " 60"
##  [480,] " 60"
##  [481,] " 80"
##  [482,] "100"
##  [483,] " 31"
##  [484,] " 71"
##  [485,] " 25"
##  [486,] " 65"
##  [487,] " 45"
##  [488,] " 60"
##  [489,] " 70"
##  [490,] " 55"
##  [491,] " 90"
##  [492,] " 58"
##  [493,] " 58"
##  [494,] " 30"
##  [495,] " 30"
##  [496,] " 36"
##  [497,] " 36"
##  [498,] " 36"
##  [499,] " 36"
##  [500,] " 66"
##  [501,] " 70"
##  [502,] " 40"
##  [503,] " 95"
##  [504,] " 85"
##  [505,] "115"
##  [506,] " 35"
##  [507,] " 85"
##  [508,] " 34"
##  [509,] " 39"
##  [510,] "115"
##  [511,] " 70"
##  [512,] " 80"
##  [513,] " 85"
##  [514,] "105"
##  [515,] "135"
##  [516,] "105"
##  [517,] " 71"
##  [518,] " 85"
##  [519,] "112"
##  [520,] " 45"
##  [521,] " 74"
##  [522,] " 84"
##  [523,] " 23"
##  [524,] " 33"
##  [525,] " 10"
##  [526,] " 60"
##  [527,] " 30"
##  [528,] " 91"
##  [529,] " 35"
##  [530,] " 42"
##  [531,] " 82"
##  [532,] "102"
##  [533,] " 92"
##  [534,] "  5"
##  [535,] " 60"
##  [536,] " 90"
##  [537,] "112"
##  [538,] " 32"
##  [539,] " 47"
##  [540,] " 65"
##  [541,] " 95"
##  [542,] " 50"
##  [543,] " 85"
##  [544,] " 46"
##  [545,] " 66"
##  [546,] " 91"
##  [547,] " 50"
##  [548,] " 40"
##  [549,] " 60"
##  [550,] " 30"
##  [551,] "125"
##  [552,] " 60"
##  [553,] " 50"
##  [554,] " 40"
##  [555,] " 50"
##  [556,] " 95"
##  [557,] " 83"
##  [558,] " 80"
##  [559,] " 95"
##  [560,] " 95"
##  [561,] " 65"
##  [562,] " 95"
##  [563,] " 80"
##  [564,] " 90"
##  [565,] " 80"
##  [566,] "110"
##  [567,] " 40"
##  [568,] " 45"
##  [569,] "110"
##  [570,] " 91"
##  [571,] " 86"
##  [572,] " 86"
##  [573,] " 86"
##  [574,] " 86"
##  [575,] " 86"
##  [576,] " 95"
##  [577,] " 80"
##  [578,] "115"
##  [579,] " 90"
##  [580,] "100"
##  [581,] " 77"
##  [582,] "100"
##  [583,] " 90"
##  [584,] " 90"
##  [585,] " 85"
##  [586,] " 80"
##  [587,] "100"
##  [588,] "125"
##  [589,] "100"
##  [590,] "127"
##  [591,] "120"
##  [592,] "100"
##  [593,] " 63"
##  [594,] " 83"
##  [595,] "113"
##  [596,] " 45"
##  [597,] " 55"
##  [598,] " 65"
##  [599,] " 45"
##  [600,] " 60"
##  [601,] " 70"
##  [602,] " 42"
##  [603,] " 77"
##  [604,] " 55"
##  [605,] " 60"
##  [606,] " 80"
##  [607,] " 66"
##  [608,] "106"
##  [609,] " 64"
##  [610,] "101"
##  [611,] " 64"
##  [612,] "101"
##  [613,] " 64"
##  [614,] "101"
##  [615,] " 24"
##  [616,] " 29"
##  [617,] " 43"
##  [618,] " 65"
##  [619,] " 93"
##  [620,] " 76"
##  [621,] "116"
##  [622,] " 15"
##  [623,] " 20"
##  [624,] " 25"
##  [625,] " 72"
##  [626,] "114"
##  [627,] " 68"
##  [628,] " 88"
##  [629,] " 50"
##  [630,] " 50"
##  [631,] " 35"
##  [632,] " 40"
##  [633,] " 45"
##  [634,] " 64"
##  [635,] " 69"
##  [636,] " 74"
##  [637,] " 45"
##  [638,] " 85"
##  [639,] " 42"
##  [640,] " 42"
##  [641,] " 92"
##  [642,] " 57"
##  [643,] " 47"
##  [644,] "112"
##  [645,] " 66"
##  [646,] "116"
##  [647,] " 30"
##  [648,] " 90"
##  [649,] " 98"
##  [650,] " 98"
##  [651,] " 65"
##  [652,] " 74"
##  [653,] " 92"
##  [654,] " 50"
##  [655,] " 50"
##  [656,] " 95"
##  [657,] " 55"
##  [658,] " 95"
##  [659,] "135"
##  [660,] " 60"
##  [661,] " 55"
##  [662,] " 45"
##  [663,] " 48"
##  [664,] " 58"
##  [665,] " 97"
##  [666,] " 30"
##  [667,] " 30"
##  [668,] " 30"
##  [669,] " 22"
##  [670,] " 32"
##  [671,] " 70"
##  [672,] "110"
##  [673,] " 65"
##  [674,] " 75"
##  [675,] " 65"
##  [676,] "105"
##  [677,] " 75"
##  [678,] "115"
##  [679,] " 45"
##  [680,] " 55"
##  [681,] " 65"
##  [682,] " 20"
##  [683,] " 30"
##  [684,] " 30"
##  [685,] " 55"
##  [686,] " 98"
##  [687,] " 44"
##  [688,] " 59"
##  [689,] " 79"
##  [690,] " 75"
##  [691,] " 95"
##  [692,] "103"
##  [693,] " 60"
##  [694,] " 20"
##  [695,] " 15"
##  [696,] " 30"
##  [697,] " 40"
##  [698,] " 60"
##  [699,] " 65"
##  [700,] " 65"
##  [701,] "108"
##  [702,] " 10"
##  [703,] " 20"
##  [704,] " 30"
##  [705,] " 50"
##  [706,] " 90"
##  [707,] " 60"
##  [708,] " 40"
##  [709,] " 50"
##  [710,] " 30"
##  [711,] " 40"
##  [712,] " 20"
##  [713,] " 55"
##  [714,] " 80"
##  [715,] " 57"
##  [716,] " 67"
##  [717,] " 97"
##  [718,] " 40"
##  [719,] " 50"
##  [720,] "105"
##  [721,] " 25"
##  [722,] "145"
##  [723,] " 32"
##  [724,] " 32"
##  [725,] " 65"
##  [726,] "105"
##  [727,] " 48"
##  [728,] " 35"
##  [729,] " 55"
##  [730,] " 60"
##  [731,] " 70"
##  [732,] " 55"
##  [733,] " 60"
##  [734,] " 80"
##  [735,] " 60"
##  [736,] " 80"
##  [737,] " 65"
##  [738,] "109"
##  [739,] " 38"
##  [740,] " 58"
##  [741,] " 98"
##  [742,] " 60"
##  [743,] "100"
##  [744,] "108"
##  [745,] "108"
##  [746,] "108"
##  [747,] "111"
##  [748,] "121"
##  [749,] "111"
##  [750,] "101"
##  [751,] " 90"
##  [752,] " 90"
##  [753,] "101"
##  [754,] " 91"
##  [755,] " 95"
##  [756,] " 95"
##  [757,] " 95"
##  [758,] "108"
##  [759,] "108"
##  [760,] " 90"
##  [761,] "128"
##  [762,] " 99"
##  [763,] " 38"
##  [764,] " 57"
##  [765,] " 64"
##  [766,] " 60"
##  [767,] " 73"
##  [768,] "104"
##  [769,] " 71"
##  [770,] " 97"
##  [771,] "122"
##  [772,] "132"
##  [773,] " 57"
##  [774,] " 78"
##  [775,] " 62"
##  [776,] " 84"
##  [777,] "126"
##  [778,] " 35"
##  [779,] " 29"
##  [780,] " 89"
##  [781,] " 72"
##  [782,] "106"
##  [783,] " 42"
##  [784,] " 52"
##  [785,] " 75"
##  [786,] " 52"
##  [787,] " 68"
##  [788,] " 43"
##  [789,] " 58"
##  [790,] "102"
##  [791,] " 68"
##  [792,] "104"
##  [793,] "104"
##  [794,] " 28"
##  [795,] " 35"
##  [796,] " 60"
##  [797,] " 60"
##  [798,] " 23"
##  [799,] " 29"
##  [800,] " 49"
##  [801,] " 72"
##  [802,] " 45"
##  [803,] " 73"
##  [804,] " 50"
##  [805,] " 68"
##  [806,] " 30"
##  [807,] " 44"
##  [808,] " 44"
##  [809,] " 59"
##  [810,] " 70"
##  [811,] "109"
##  [812,] " 48"
##  [813,] " 71"
##  [814,] " 46"
##  [815,] " 58"
##  [816,] " 60"
##  [817,] "118"
##  [818,] "101"
##  [819,] " 50"
##  [820,] " 40"
##  [821,] " 60"
##  [822,] " 80"
##  [823,] " 75"
##  [824,] " 38"
##  [825,] " 56"
##  [826,] " 51"
##  [827,] " 56"
##  [828,] " 46"
##  [829,] " 41"
##  [830,] " 84"
##  [831,] " 99"
##  [832,] " 69"
##  [833,] " 54"
##  [834,] " 28"
##  [835,] " 28"
##  [836,] " 55"
##  [837,] "123"
##  [838,] " 99"
##  [839,] " 99"
##  [840,] " 95"
##  [841,] "115"
##  [842,] " 85"
##  [843,] " 50"
##  [844,] "110"
##  [845,] " 70"
##  [846,] " 80"
##  [847,] " 70"
##  [848,] " 42"
##  [849,] " 52"
##  [850,] " 70"
##  [851,] " 70"
##  [852,] " 90"
##  [853,] " 60"
##  [854,] " 40"
##  [855,] " 50"
##  [856,] " 60"
##  [857,] " 65"
##  [858,] " 75"
##  [859,] " 60"
##  [860,] " 45"
##  [861,] " 45"
##  [862,] " 46"
##  [863,] " 36"
##  [864,] " 43"
##  [865,] " 63"
##  [866,] " 43"
##  [867,] " 93"
##  [868,] " 93"
##  [869,] " 93"
##  [870,] " 93"
##  [871,] " 84"
##  [872,] "124"
##  [873,] " 60"
##  [874,] " 60"
##  [875,] "112"
##  [876,] " 82"
##  [877,] "110"
##  [878,] " 40"
##  [879,] " 30"
##  [880,] " 45"
##  [881,] " 35"
##  [882,] " 45"
##  [883,] " 35"
##  [884,] " 27"
##  [885,] " 42"
##  [886,] " 35"
##  [887,] " 45"
##  [888,] " 15"
##  [889,] " 30"
##  [890,] " 77"
##  [891,] "117"
##  [892,] " 50"
##  [893,] " 60"
##  [894,] " 32"
##  [895,] " 62"
##  [896,] " 72"
##  [897,] "100"
##  [898,] " 60"
##  [899,] " 80"
##  [900,] " 80"
##  [901,] " 40"
##  [902,] " 15"
##  [903,] " 35"
##  [904,] "  5"
##  [905,] " 59"
##  [906,] " 95"
##  [907,] " 60"
##  [908,] "120"
##  [909,] " 65"
##  [910,] " 36"
##  [911,] " 96"
##  [912,] " 96"
##  [913,] " 92"
##  [914,] " 36"
##  [915,] " 40"
##  [916,] " 45"
##  [917,] " 65"
##  [918,] " 85"
##  [919,] "130"
##  [920,] " 95"
##  [921,] " 75"
##  [922,] " 85"
##  [923,] " 37"
##  [924,] " 37"
##  [925,] " 97"
##  [926,] " 97"
##  [927,] "103"
##  [928,] " 79"
##  [929,] "151"
##  [930,] " 83"
##  [931,] " 61"
##  [932,] "109"
##  [933,] " 43"
##  [934,] " 79"
##  [935,] " 77"
##  [936,] " 77"
##  [937,] "129"
##  [938,] " 65"
##  [939,] "125"
##  [940,] " 73"
##  [941,] "121"
##  [942,] " 13"
##  [943,] "107"
##  [944,] "143"
##  [945,] " 34"
##  [946,] " 34"
##  [947,] " 65"
##  [948,] " 80"
##  [949,] " 85"
##  [950,] " 69"
##  [951,] " 94"
##  [952,] "119"
##  [953,] " 70"
##  [954,] " 90"
##  [955,] "120"
##  [956,] " 25"
##  [957,] " 20"
##  [958,] " 57"
##  [959,] " 77"
##  [960,] " 67"
##  [961,] " 45"
##  [962,] " 30"
##  [963,] " 90"
##  [964,] " 50"
##  [965,] " 90"
##  [966,] " 10"
##  [967,] " 60"
##  [968,] " 48"
##  [969,] " 88"
##  [970,] " 44"
##  [971,] " 74"
##  [972,] " 26"
##  [973,] "121"
##  [974,] " 30"
##  [975,] " 50"
##  [976,] " 30"
##  [977,] " 20"
##  [978,] " 70"
##  [979,] " 30"
##  [980,] " 46"
##  [981,] " 71"
##  [982,] " 85"
##  [983,] " 66"
##  [984,] "136"
##  [985,] " 40"
##  [986,] " 75"
##  [987,] " 75"
##  [988,] " 45"
##  [989,] " 65"
##  [990,] " 32"
##  [991,] " 42"
##  [992,] " 50"
##  [993,] " 70"
##  [994,] " 39"
##  [995,] " 49"
##  [996,] " 29"
##  [997,] " 50"
##  [998,] " 70"
##  [999,] " 60"
## [1000,] " 95"
## [1001,] " 50"
## [1002,] " 30"
## [1003,] " 65"
## [1004,] " 70"
## [1005,] " 30"
## [1006,] " 34"
## [1007,] " 64"
## [1008,] " 75"
## [1009,] " 15"
## [1010,] " 20"
## [1011,] " 65"
## [1012,] " 70"
## [1013,] " 50"
## [1014,] "130"
## [1015,] " 95"
## [1016,] " 85"
## [1017,] " 97"
## [1018,] " 97"
## [1019,] " 40"
## [1020,] " 30"
## [1021,] " 75"
## [1022,] " 55"
## [1023,] " 75"
## [1024,] " 55"
## [1025,] " 85"
## [1026,] " 82"
## [1027,] "102"
## [1028,] "142"
## [1029,] "148"
## [1030,] "138"
## [1031,] "128"
## [1032,] "138"
## [1033,] "130"
## [1034,] "130"
## [1035,] " 72"
## [1036,] " 97"
## [1037,] " 97"
## [1038,] "105"
## [1039,] "200"
## [1040,] " 80"
## [1041,] " 30"
## [1042,] "130"
## [1043,] " 80"
## [1044,] " 50"
## [1045,] "150"
plot(d2$HP)

plot(d2$Total)

plot(d2$Attack)

plot(d2$Defence)

plot(d2$Sp_attack)

plot(d2$Sp_defence)

plot(d2$Speed)

plot(md)

stem(d2$HP)
## 
##   The decimal point is 1 digit(s) to the right of the |
## 
##    0 | 100
##    2 | 0000005555880000000000000000155555555555555555678888888888899
##    4 | 00000000000000000000000000000000000000000000000011112222333334444444+204
##    6 | 00000000000000000000000000000000000000000000000000000000000000000000+299
##    8 | 00000000000000000000000000000000000000000000000000000012233334455555+107
##   10 | 00000000000000000000000000000000000000000000133345555555555555566666+13
##   12 | 000002335555660005577
##   14 | 0040000
##   16 | 050
##   18 | 0
##   20 | 06
##   22 | 3
##   24 | 055
stem(d2$Total)
## 
##   The decimal point is 2 digit(s) to the right of the |
## 
##    1 | 888999
##    2 | 00000000111111111122222223444444444
##    2 | 55555555555555555556666666667777777777777777788888888888888888888999+8
##    3 | 00000000000000000000000000000000000000011111111111111111111111111111+86
##    3 | 55555555555555566666666666666666777777777777788888888899999999999999
##    4 | 00000000000000111111111111111111111111111111111111111111111112222222+47
##    4 | 55555555555555555566666666666666666666666666666666667777777777777777+114
##    5 | 00000000000000000000000000000000000000000000000000000000000011111111+109
##    5 | 55555566667777777777777777888888888888888888888888888888889999
##    6 | 000000000000000000000000000000000000000000000011222233333333444
##    6 | 677777788888888888888888889
##    7 | 0000000001222
##    7 | 577888
##    8 | 
##    8 | 
##    9 | 
##    9 | 
##   10 | 
##   10 | 
##   11 | 3
stem(d2$Attack)
## 
##   The decimal point is 1 digit(s) to the right of the |
## 
##    0 | 55
##    1 | 0005
##    2 | 00000000002345555555578999
##    3 | 0000000000000000000000035555555555555555568888
##    4 | 00000000000000000000000000000011123445555555555555555555555555555555+7
##    5 | 00000000000000000000000000000000000000112222222233333334455555555555+32
##    6 | 00000000000000000000000000000000000000000001122222333333334444444555+57
##    7 | 00000000000000000000000000000000000000000011122222223333333445555555+35
##    8 | 00000000000000000000000000000000000000000000000111122222222333344444+45
##    9 | 00000000000000000000000000000000000000001222222223344555555555555555+17
##   10 | 00000000000000000000000000000000000000000000000000000011123334445555+14
##   11 | 0000000000000000000002223355555555555555555677778
##   12 | 00000000000000000000000000001333344555555555555555555555789
##   13 | 00000000000000000000001111224455555556779
##   14 | 00000000035555557
##   15 | 0000000000557
##   16 | 000000455557
##   17 | 000
##   18 | 00015
##   19 | 0
stem(d2$Defence)
## 
##   The decimal point is 1 digit(s) to the right of the |
## 
##    0 | 55
##    1 | 05555
##    2 | 00000035588
##    3 | 000000000000000000122344455555555555555555555555555555777788899
##    4 | 00000000000000000000000000000000000000000000000111222333333444455555+39
##    5 | 00000000000000000000000000000000000000000000000000000000000000001222+59
##    6 | 00000000000000000000000000000000000000000000000000000000000011222222+71
##    7 | 00000000000000000000000000000000000000000000000000000000000000000000+64
##    8 | 00000000000000000000000000000000000000000000000000000233344555555555+24
##    9 | 00000000000000000000000000000000000000000000000000011245555555555555+16
##   10 | 00000000000000000000000000000000000000000000011233555555555555555556
##   11 | 0000000000000001255555555555555555556889
##   12 | 00000000000000001122222355555555679
##   13 | 00000000000000111355559
##   14 | 0000000035555
##   15 | 00000002
##   16 | 0008
##   17 | 
##   18 | 0004
##   19 | 
##   20 | 00
##   21 | 1
##   22 | 
##   23 | 000
##   24 | 
##   25 | 0
stem(d2$Sp_attack)
## 
##   The decimal point is 1 digit(s) to the right of the |
## 
##    1 | 000055555
##    2 | 000000000034455555555555555577999
##    3 | 00000000000000000000000000000000000122335555555555555555555555555555
##    4 | 00000000000000000000000000000000000000000000000000000000000000000000+53
##    5 | 00000000000000000000000000000000000000000000000000000013333333334444+58
##    6 | 00000000000000000000000000000000000000000000000000000000000011111112+67
##    7 | 00000000000000000000000000000000000000000001112223333344444455555555+13
##    8 | 00000000000000000000000000000000000000000011111112333333335555555555+17
##    9 | 00000000000000000000000000000111112222344555555555555555555555555555+13
##   10 | 00000000000000000000000000000000000000233455555555555555555555567789
##   11 | 000000000000000000011223344445555555556
##   12 | 00000000000000025555555555555555556777899
##   13 | 0000000000000112455555555677
##   14 | 00000055555555
##   15 | 00000000134479
##   16 | 005557
##   17 | 00035
##   18 | 000
##   19 | 4
stem(d2$Sp_defence)
## 
##   The decimal point is 1 digit(s) to the right of the |
## 
##    2 | 00000003555555555555
##    3 | 00000000000000000000000000011123455555555555555555555555556777788899
##    4 | 00000000000000000000000000000000000000000001111122222333444555555555+38
##    5 | 00000000000000000000000000000000000000000000000000000000000000000001+73
##    6 | 00000000000000000000000000000000000000000000000000000000011111122223+63
##    7 | 00000000000000000000000000000000000000000000000000000000000001111112+57
##    8 | 00000000000000000000000000000000000000000000000000000000000001111122+46
##    9 | 00000000000000000000000000000000000000000000000000012245555555555555+22
##   10 | 00000000000000000000000000000000000001112355555555555555555555556777
##   11 | 000000000000000000034555555555555555666
##   12 | 000000000000000135556789
##   13 | 0000000000000112555558
##   14 | 00025
##   15 | 000000444
##   16 | 00
##   17 | 
##   18 | 
##   19 | 
##   20 | 0
##   21 | 
##   22 | 
##   23 | 0
##   24 | 
##   25 | 0
stem(d2$Speed)
## 
##   The decimal point is 1 digit(s) to the right of the |
## 
##    0 | 555
##    1 | 000035555555555555
##    2 | 00000000000000000002333345555555555556788889999
##    3 | 00000000000000000000000000000000000000000000000001122222223344445555+26
##    4 | 00000000000000000000000000000000000000001112222222223333333344445555+40
##    5 | 00000000000000000000000000000000000000000000000000000000011122222455+40
##    6 | 00000000000000000000000000000000000000000000000000000001112233344444+54
##    7 | 00000000000000000000000000000000000000000000000111111122222223334444+29
##    8 | 00000000000000000000000000000000000000011112223333444455555555555555+17
##    9 | 00000000000000000000000000000000000000001111122223333334555555555555+30
##   10 | 00000000000000000000000000000000001111111222334445555555555555566788
##   11 | 000000000000000000011222234555555555555566789
##   12 | 000000001111234555567889
##   13 | 000000000002555688
##   14 | 0235558
##   15 | 0000001
##   16 | 0
##   17 | 
##   18 | 0
##   19 | 
##   20 | 0
stem(d2$HP, scale =2)
## 
##   The decimal point is 1 digit(s) to the right of the |
## 
##    0 | 1
##    1 | 00
##    2 | 000000555588
##    3 | 0000000000000000155555555555555555678888888888899
##    4 | 00000000000000000000000000000000000000000000000011112222333334444444+49
##    5 | 00000000000000000000000000000000000000000000000000000000000000000000+75
##    6 | 00000000000000000000000000000000000000000000000000000000000000000000+109
##    7 | 00000000000000000000000000000000000000000000000000000000000000000000+110
##    8 | 00000000000000000000000000000000000000000000000000000012233334455555+16
##    9 | 00000000000000000000000000000000000000001111111222223555555555555555+11
##   10 | 00000000000000000000000000000000000000000000133345555555555555566666
##   11 | 00000000000145556
##   12 | 00000233555566
##   13 | 0005577
##   14 | 004
##   15 | 0000
##   16 | 05
##   17 | 0
##   18 | 
##   19 | 0
##   20 | 0
##   21 | 6
##   22 | 3
##   23 | 
##   24 | 
##   25 | 055
hist(d2$HP)

hist(d2$Total)

hist(d2$Attack)

hist(d2$Defence)

hist(d2$Sp_attack)

hist(d2$Sp_defence)

hist(d2$Speed)

hist(d2$HP, breaks = 'Sturges')

hist(d2$HP, breaks = 'Scott')

hist(d2$HP, breaks = 'FD')

hist(d2$HP, breaks = 'st')

hist(d2$HP, breaks = 'sc')

hist(d2$HP, breaks = 7)

hist(d2$HP, col = 'gray75', main = NULL, xlab = 'Size class of d', ylim = c(0,0.1), freq = FALSE)

dens = density(d2$HP)
dens
## 
## Call:
##  density.default(x = d2$HP)
## 
## Data: d2$HP (1045 obs.); Bandwidth 'bw' = 5.351
## 
##        x                y            
##  Min.   :-15.05   Min.   :8.180e-07  
##  1st Qu.: 56.47   1st Qu.:7.976e-05  
##  Median :128.00   Median :2.725e-04  
##  Mean   :128.00   Mean   :3.492e-03  
##  3rd Qu.:199.53   3rd Qu.:5.412e-03  
##  Max.   :271.05   Max.   :1.765e-02

The class() command to know the type of object.

class(d2)
## [1] "data.frame"
class(dimnames(d2))
## [1] "list"
class(d2$HP)
## [1] "integer"

The str() command to examine the structure of an object.

str(d2)
## 'data.frame':    1045 obs. of  8 variables:
##  $ Name      : chr  "Bulbasaur" "Ivysaur" "Venusaur" "Mega Venusaur" ...
##  $ Total     : int  318 405 525 625 309 405 534 634 634 314 ...
##  $ HP        : int  45 60 80 80 39 58 78 78 78 44 ...
##  $ Attack    : int  49 62 82 100 52 64 84 130 104 48 ...
##  $ Defence   : int  49 63 83 123 43 58 78 111 78 65 ...
##  $ Sp_attack : int  65 80 100 122 60 80 109 130 159 50 ...
##  $ Sp_defence: int  65 80 100 120 50 65 85 85 115 64 ...
##  $ Speed     : int  45 60 80 80 65 80 100 100 100 43 ...
str(d2$HP)
##  int [1:1045] 45 60 80 80 39 58 78 78 78 44 ...
structure(head(d2))
ls.str(pattern = 'd2')
## d2 : 'data.frame':   1045 obs. of  8 variables:
##  $ Name      : chr  "Bulbasaur" "Ivysaur" "Venusaur" "Mega Venusaur" ...
##  $ Total     : int  318 405 525 625 309 405 534 634 634 314 ...
##  $ HP        : int  45 60 80 80 39 58 78 78 78 44 ...
##  $ Attack    : int  49 62 82 100 52 64 84 130 104 48 ...
##  $ Defence   : int  49 63 83 123 43 58 78 111 78 65 ...
##  $ Sp_attack : int  65 80 100 122 60 80 109 130 159 50 ...
##  $ Sp_defence: int  65 80 100 120 50 65 85 85 115 64 ...
##  $ Speed     : int  45 60 80 80 65 80 100 100 100 43 ...
attach(head(d2))
stack(head(d2))
dimnames(d2)[[1]]
##    [1] "1"    "2"    "3"    "4"    "5"    "6"    "7"    "8"    "9"    "10"  
##   [11] "11"   "12"   "13"   "14"   "15"   "16"   "17"   "18"   "19"   "20"  
##   [21] "21"   "22"   "23"   "24"   "25"   "26"   "27"   "28"   "29"   "30"  
##   [31] "31"   "32"   "33"   "34"   "35"   "36"   "37"   "38"   "39"   "40"  
##   [41] "41"   "42"   "43"   "44"   "45"   "46"   "47"   "48"   "49"   "50"  
##   [51] "51"   "52"   "53"   "54"   "55"   "56"   "57"   "58"   "59"   "60"  
##   [61] "61"   "62"   "63"   "64"   "65"   "66"   "67"   "68"   "69"   "70"  
##   [71] "71"   "72"   "73"   "74"   "75"   "76"   "77"   "78"   "79"   "80"  
##   [81] "81"   "82"   "83"   "84"   "85"   "86"   "87"   "88"   "89"   "90"  
##   [91] "91"   "92"   "93"   "94"   "95"   "96"   "97"   "98"   "99"   "100" 
##  [101] "101"  "102"  "103"  "104"  "105"  "106"  "107"  "108"  "109"  "110" 
##  [111] "111"  "112"  "113"  "114"  "115"  "116"  "117"  "118"  "119"  "120" 
##  [121] "121"  "122"  "123"  "124"  "125"  "126"  "127"  "128"  "129"  "130" 
##  [131] "131"  "132"  "133"  "134"  "135"  "136"  "137"  "138"  "139"  "140" 
##  [141] "141"  "142"  "143"  "144"  "145"  "146"  "147"  "148"  "149"  "150" 
##  [151] "151"  "152"  "153"  "154"  "155"  "156"  "157"  "158"  "159"  "160" 
##  [161] "161"  "162"  "163"  "164"  "165"  "166"  "167"  "168"  "169"  "170" 
##  [171] "171"  "172"  "173"  "174"  "175"  "176"  "177"  "178"  "179"  "180" 
##  [181] "181"  "182"  "183"  "184"  "185"  "186"  "187"  "188"  "189"  "190" 
##  [191] "191"  "192"  "193"  "194"  "195"  "196"  "197"  "198"  "199"  "200" 
##  [201] "201"  "202"  "203"  "204"  "205"  "206"  "207"  "208"  "209"  "210" 
##  [211] "211"  "212"  "213"  "214"  "215"  "216"  "217"  "218"  "219"  "220" 
##  [221] "221"  "222"  "223"  "224"  "225"  "226"  "227"  "228"  "229"  "230" 
##  [231] "231"  "232"  "233"  "234"  "235"  "236"  "237"  "238"  "239"  "240" 
##  [241] "241"  "242"  "243"  "244"  "245"  "246"  "247"  "248"  "249"  "250" 
##  [251] "251"  "252"  "253"  "254"  "255"  "256"  "257"  "258"  "259"  "260" 
##  [261] "261"  "262"  "263"  "264"  "265"  "266"  "267"  "268"  "269"  "270" 
##  [271] "271"  "272"  "273"  "274"  "275"  "276"  "277"  "278"  "279"  "280" 
##  [281] "281"  "282"  "283"  "284"  "285"  "286"  "287"  "288"  "289"  "290" 
##  [291] "291"  "292"  "293"  "294"  "295"  "296"  "297"  "298"  "299"  "300" 
##  [301] "301"  "302"  "303"  "304"  "305"  "306"  "307"  "308"  "309"  "310" 
##  [311] "311"  "312"  "313"  "314"  "315"  "316"  "317"  "318"  "319"  "320" 
##  [321] "321"  "322"  "323"  "324"  "325"  "326"  "327"  "328"  "329"  "330" 
##  [331] "331"  "332"  "333"  "334"  "335"  "336"  "337"  "338"  "339"  "340" 
##  [341] "341"  "342"  "343"  "344"  "345"  "346"  "347"  "348"  "349"  "350" 
##  [351] "351"  "352"  "353"  "354"  "355"  "356"  "357"  "358"  "359"  "360" 
##  [361] "361"  "362"  "363"  "364"  "365"  "366"  "367"  "368"  "369"  "370" 
##  [371] "371"  "372"  "373"  "374"  "375"  "376"  "377"  "378"  "379"  "380" 
##  [381] "381"  "382"  "383"  "384"  "385"  "386"  "387"  "388"  "389"  "390" 
##  [391] "391"  "392"  "393"  "394"  "395"  "396"  "397"  "398"  "399"  "400" 
##  [401] "401"  "402"  "403"  "404"  "405"  "406"  "407"  "408"  "409"  "410" 
##  [411] "411"  "412"  "413"  "414"  "415"  "416"  "417"  "418"  "419"  "420" 
##  [421] "421"  "422"  "423"  "424"  "425"  "426"  "427"  "428"  "429"  "430" 
##  [431] "431"  "432"  "433"  "434"  "435"  "436"  "437"  "438"  "439"  "440" 
##  [441] "441"  "442"  "443"  "444"  "445"  "446"  "447"  "448"  "449"  "450" 
##  [451] "451"  "452"  "453"  "454"  "455"  "456"  "457"  "458"  "459"  "460" 
##  [461] "461"  "462"  "463"  "464"  "465"  "466"  "467"  "468"  "469"  "470" 
##  [471] "471"  "472"  "473"  "474"  "475"  "476"  "477"  "478"  "479"  "480" 
##  [481] "481"  "482"  "483"  "484"  "485"  "486"  "487"  "488"  "489"  "490" 
##  [491] "491"  "492"  "493"  "494"  "495"  "496"  "497"  "498"  "499"  "500" 
##  [501] "501"  "502"  "503"  "504"  "505"  "506"  "507"  "508"  "509"  "510" 
##  [511] "511"  "512"  "513"  "514"  "515"  "516"  "517"  "518"  "519"  "520" 
##  [521] "521"  "522"  "523"  "524"  "525"  "526"  "527"  "528"  "529"  "530" 
##  [531] "531"  "532"  "533"  "534"  "535"  "536"  "537"  "538"  "539"  "540" 
##  [541] "541"  "542"  "543"  "544"  "545"  "546"  "547"  "548"  "549"  "550" 
##  [551] "551"  "552"  "553"  "554"  "555"  "556"  "557"  "558"  "559"  "560" 
##  [561] "561"  "562"  "563"  "564"  "565"  "566"  "567"  "568"  "569"  "570" 
##  [571] "571"  "572"  "573"  "574"  "575"  "576"  "577"  "578"  "579"  "580" 
##  [581] "581"  "582"  "583"  "584"  "585"  "586"  "587"  "588"  "589"  "590" 
##  [591] "591"  "592"  "593"  "594"  "595"  "596"  "597"  "598"  "599"  "600" 
##  [601] "601"  "602"  "603"  "604"  "605"  "606"  "607"  "608"  "609"  "610" 
##  [611] "611"  "612"  "613"  "614"  "615"  "616"  "617"  "618"  "619"  "620" 
##  [621] "621"  "622"  "623"  "624"  "625"  "626"  "627"  "628"  "629"  "630" 
##  [631] "631"  "632"  "633"  "634"  "635"  "636"  "637"  "638"  "639"  "640" 
##  [641] "641"  "642"  "643"  "644"  "645"  "646"  "647"  "648"  "649"  "650" 
##  [651] "651"  "652"  "653"  "654"  "655"  "656"  "657"  "658"  "659"  "660" 
##  [661] "661"  "662"  "663"  "664"  "665"  "666"  "667"  "668"  "669"  "670" 
##  [671] "671"  "672"  "673"  "674"  "675"  "676"  "677"  "678"  "679"  "680" 
##  [681] "681"  "682"  "683"  "684"  "685"  "686"  "687"  "688"  "689"  "690" 
##  [691] "691"  "692"  "693"  "694"  "695"  "696"  "697"  "698"  "699"  "700" 
##  [701] "701"  "702"  "703"  "704"  "705"  "706"  "707"  "708"  "709"  "710" 
##  [711] "711"  "712"  "713"  "714"  "715"  "716"  "717"  "718"  "719"  "720" 
##  [721] "721"  "722"  "723"  "724"  "725"  "726"  "727"  "728"  "729"  "730" 
##  [731] "731"  "732"  "733"  "734"  "735"  "736"  "737"  "738"  "739"  "740" 
##  [741] "741"  "742"  "743"  "744"  "745"  "746"  "747"  "748"  "749"  "750" 
##  [751] "751"  "752"  "753"  "754"  "755"  "756"  "757"  "758"  "759"  "760" 
##  [761] "761"  "762"  "763"  "764"  "765"  "766"  "767"  "768"  "769"  "770" 
##  [771] "771"  "772"  "773"  "774"  "775"  "776"  "777"  "778"  "779"  "780" 
##  [781] "781"  "782"  "783"  "784"  "785"  "786"  "787"  "788"  "789"  "790" 
##  [791] "791"  "792"  "793"  "794"  "795"  "796"  "797"  "798"  "799"  "800" 
##  [801] "801"  "802"  "803"  "804"  "805"  "806"  "807"  "808"  "809"  "810" 
##  [811] "811"  "812"  "813"  "814"  "815"  "816"  "817"  "818"  "819"  "820" 
##  [821] "821"  "822"  "823"  "824"  "825"  "826"  "827"  "828"  "829"  "830" 
##  [831] "831"  "832"  "833"  "834"  "835"  "836"  "837"  "838"  "839"  "840" 
##  [841] "841"  "842"  "843"  "844"  "845"  "846"  "847"  "848"  "849"  "850" 
##  [851] "851"  "852"  "853"  "854"  "855"  "856"  "857"  "858"  "859"  "860" 
##  [861] "861"  "862"  "863"  "864"  "865"  "866"  "867"  "868"  "869"  "870" 
##  [871] "871"  "872"  "873"  "874"  "875"  "876"  "877"  "878"  "879"  "880" 
##  [881] "881"  "882"  "883"  "884"  "885"  "886"  "887"  "888"  "889"  "890" 
##  [891] "891"  "892"  "893"  "894"  "895"  "896"  "897"  "898"  "899"  "900" 
##  [901] "901"  "902"  "903"  "904"  "905"  "906"  "907"  "908"  "909"  "910" 
##  [911] "911"  "912"  "913"  "914"  "915"  "916"  "917"  "918"  "919"  "920" 
##  [921] "921"  "922"  "923"  "924"  "925"  "926"  "927"  "928"  "929"  "930" 
##  [931] "931"  "932"  "933"  "934"  "935"  "936"  "937"  "938"  "939"  "940" 
##  [941] "941"  "942"  "943"  "944"  "945"  "946"  "947"  "948"  "949"  "950" 
##  [951] "951"  "952"  "953"  "954"  "955"  "956"  "957"  "958"  "959"  "960" 
##  [961] "961"  "962"  "963"  "964"  "965"  "966"  "967"  "968"  "969"  "970" 
##  [971] "971"  "972"  "973"  "974"  "975"  "976"  "977"  "978"  "979"  "980" 
##  [981] "981"  "982"  "983"  "984"  "985"  "986"  "987"  "988"  "989"  "990" 
##  [991] "991"  "992"  "993"  "994"  "995"  "996"  "997"  "998"  "999"  "1000"
## [1001] "1001" "1002" "1003" "1004" "1005" "1006" "1007" "1008" "1009" "1010"
## [1011] "1011" "1012" "1013" "1014" "1015" "1016" "1017" "1018" "1019" "1020"
## [1021] "1021" "1022" "1023" "1024" "1025" "1026" "1027" "1028" "1029" "1030"
## [1031] "1031" "1032" "1033" "1034" "1035" "1036" "1037" "1038" "1039" "1040"
## [1041] "1041" "1042" "1043" "1044" "1045"
dimnames(d2)[[2]]
## [1] "Name"       "Total"      "HP"         "Attack"     "Defence"   
## [6] "Sp_attack"  "Sp_defence" "Speed"
head(d2, n=10)

Simple math:

search()
##  [1] ".GlobalEnv"        "head(d2)"          "package:stats"    
##  [4] "package:graphics"  "package:grDevices" "package:utils"    
##  [7] "package:datasets"  "package:methods"   "Autoloads"        
## [10] "package:base"
sin(d2$HP)
##    [1]  0.85090352 -0.30481062 -0.99388865 -0.99388865  0.96379539  0.99287265
##    [7]  0.51397846  0.51397846  0.51397846  0.01770193  0.63673801 -0.44411267
##   [13] -0.44411267  0.85090352 -0.26237485 -0.30481062  0.74511316  0.85090352
##   [19]  0.82682868  0.82682868  0.74511316  0.16735570  0.96836446  0.96836446
##   [25] -0.98803162 -0.98803162 -0.99975517 -0.38778164  0.74511316  0.82682868
##   [31] -0.42818267 -0.30481062 -0.42818267  0.85090352 -0.30481062 -0.30481062
##   [37] -0.26237485 -0.26237485 -0.38778164 -0.38778164 -0.99975517  0.77389068
##   [43]  0.89399666  0.90178835 -0.96611777 -0.62988799  0.77389068  0.68326171
##   [49]  0.29636858  0.29636858 -0.67677196 -0.67677196  0.94543533  0.98023966
##   [55]  0.74511316 -0.38778164  0.85090352 -0.30481062 -0.38778164 -0.42818267
##   [61] -0.30481062 -0.30481062  0.77389068 -0.54402111 -0.54402111 -0.42818267
##   [67] -0.42818267  0.74511316  0.74511316 -0.26237485  0.82682868  0.82682868
##   [73] -0.26237485 -0.99388865  0.74511316  0.82682868 -0.99975517  0.89399666
##   [79]  0.74511316  0.82682868  0.89399666 -0.13235175  0.74511316 -0.99975517
##   [85] -0.99975517  0.77389068 -0.99388865  0.89399666 -0.26237485  0.82682868
##   [91] -0.99388865  0.74511316 -0.99388865  0.74511316  0.74511316 -0.99975517
##   [97] -0.99975517 -0.99388865 -0.99388865 -0.26237485 -0.26237485  0.82682868
##  [103]  0.82682868  0.89399666  0.89399666  0.68326171  0.68326171  0.68326171
##  [109] -0.13235175 -0.26237485  0.98662759  0.98662759 -0.42818267 -0.30481062
##  [115]  0.82682868  0.89399666 -0.99388865 -0.99388865 -0.97053528 -0.97053528
##  [121] -0.98803162 -0.26237485 -0.98803162  0.85090352 -0.30481062 -0.30481062
##  [127] -0.42818267 -0.30481062 -0.17607562 -0.98803162 -0.99975517  0.74511316
##  [133] -0.30481062 -0.30481062  0.68326171  0.68326171 -0.26237485 -0.30481062
##  [139] -0.30481062 -0.26237485 -0.26237485  0.89399666  0.74511316  0.82682868
##  [145]  0.82682868 -0.99388865 -0.97053528 -0.97052802  0.82682868 -0.97053528
##  [151] -0.97053528 -0.98803162 -0.99975517  0.85090352 -0.99388865 -0.98803162
##  [157] -0.30481062  0.74511316 -0.26237485  0.77389068  0.82682868  0.82682868
##  [163]  0.82682868  0.82682868  0.82682868 -0.38778164  0.91294525  0.68326171
##  [169]  0.68326171 -0.93010595 -0.76825466 -0.99975517  0.82682868 -0.93010595
##  [175]  0.82682868  0.82682868  0.82682868 -0.42818267  0.77389068 -0.98803162
##  [181] -0.30481062 -0.99388865 -0.99388865  0.21942526  0.89399666  0.89399666
##  [187]  0.89399666  0.89399666  0.89399666  0.89399666 -0.15862267 -0.96611777
##  [193]  0.10598751 -0.72714250 -0.72714250 -0.72714250 -0.50636564  0.85090352
##  [199] -0.30481062 -0.99388865  0.96379539  0.99287265  0.51397846 -0.26237485
##  [205]  0.82682868 -0.17607562 -0.42818267 -0.17607562 -0.30481062 -0.50636564
##  [211]  0.74511316 -0.99975517  0.74511316  0.77389068 -0.17607562 -0.38778164
##  [217] -0.61604046  0.91294525 -0.26237485  0.89399666 -0.42818267 -0.99975517
##  [223]  0.74511316  0.82682868 -0.99975517  0.77389068  0.89399666  0.89399666
##  [229] -0.38778164  0.77389068 -0.50636564  0.77389068  0.89399666 -0.42818267
##  [235] -0.99975517 -0.38778164 -0.99975517 -0.98803162 -0.38778164  0.82682868
##  [241] -0.99975517  0.68326171  0.82682868  0.68326171 -0.30481062  0.68326171
##  [247]  0.68326171 -0.30481062 -0.76825466  0.99779928  0.77389068 -0.26237485
##  [253] -0.38778164 -0.50636564  0.82682868 -0.38778164 -0.38778164 -0.30481062
##  [259]  0.89399666  0.82682868  0.77389068  0.77389068  0.91294525 -0.99388865
##  [265] -0.99388865 -0.99975517 -0.30481062  0.89399666  0.74511316 -0.30481062
##  [271] -0.26237485 -0.50636564  0.82682868 -0.30481062 -0.42818267 -0.38778164
##  [277]  0.85090352 -0.17607562  0.82682868  0.85090352 -0.38778164 -0.38778164
##  [283] -0.38778164  0.89399666  0.89399666 -0.17607562 -0.67677196 -0.99975517
##  [289] -0.42818267 -0.26237485  0.85090352  0.85090352  0.85090352  0.68326171
##  [295] -0.50639163  0.89399666  0.94543533 -0.50636564 -0.26237485  0.77389068
##  [301] -0.50636564 -0.50636564 -0.72714250 -0.72714250 -0.50636564  0.74511316
##  [307] -0.26237485  0.77389068  0.77389068  0.85090352 -0.30481062 -0.99388865
##  [313] -0.99388865 -0.26237485  0.77389068 -0.50636564 -0.50636564 -0.42818267
##  [319]  0.77389068  0.29636858  0.29636858  0.51397846  0.51397846  0.85090352
##  [325] -0.26237485 -0.30481062 -0.26237485 -0.30481062  0.74511316 -0.30481062
##  [331] -0.99388865  0.74511316  0.77389068  0.89399666  0.74511316 -0.30481062
##  [337]  0.74511316 -0.30481062  0.27090579  0.29636858 -0.89792768 -0.89792768
##  [343]  0.74511316  0.77389068 -0.30481062 -0.30481062 -0.30481062 -0.99388865
##  [349] -0.71487643 -0.40403765 -0.96611777  0.84147098  0.92002604  0.73319032
##  [355] -0.32162240  0.25382336 -0.49102159 -0.26237485 -0.98803162 -0.26237485
##  [361]  0.77389068 -0.26237485 -0.26237485 -0.26237485 -0.26237485 -0.26237485
##  [367] -0.30481062  0.77389068  0.77389068 -0.98803162 -0.30481062 -0.30481062
##  [373]  0.74511316  0.77389068  0.77389068 -0.30481062 -0.30481062  0.82682868
##  [379]  0.82682868 -0.26237485  0.77389068 -0.50636564  0.85090352  0.77389068
##  [385]  0.77389068 -0.93010595  0.34664946 -0.30481062  0.77389068  0.77389068
##  [391]  0.77389068 -0.30481062 -0.99388865 -0.30481062  0.85090352 -0.26237485
##  [397] -0.99388865 -0.26237485  0.77389068  0.85090352 -0.38778164 -0.38778164
##  [403] -0.67677196 -0.67677196  0.89399666  0.89399666 -0.26237485 -0.04424268
##  [409] -0.83177474  0.16735570  0.74511316 -0.30481062 -0.02655115 -0.92345845
##  [415]  0.85090352 -0.38778164  0.91294525  0.68326171  0.77389068  0.77389068
##  [421]  0.77389068  0.77389068 -0.30481062  0.01770193  0.92002604  0.92002604
##  [427]  0.91294525  0.74511316 -0.99920683 -0.38778164  0.82682868  0.82682868
##  [433]  0.68326171 -0.26237485 -0.99388865 -0.99388865  0.77389068  0.89399666
##  [439] -0.04424268 -0.42818267 -0.99975517 -0.99975517 -0.50636564 -0.83177474
##  [445]  0.85090352  0.82682868  0.68326171  0.68326171  0.74511316 -0.30481062
##  [451] -0.99388865 -0.99388865 -0.99388865 -0.99388865 -0.99388865 -0.99388865
##  [457] -0.99388865 -0.99388865 -0.99388865 -0.50636564 -0.50636564 -0.50636564
##  [463] -0.50636564 -0.97053528 -0.97053528 -0.50636564 -0.26237485 -0.26237485
##  [469] -0.26237485 -0.26237485 -0.99975517 -0.38778164  0.68326171  0.01770193
##  [475]  0.92002604  0.56610764  0.39592515  0.92002604  0.73319032  0.74511316
##  [481] -0.99975517 -0.17607562  0.63673801 -0.44411267 -0.64353813  0.99952016
##  [487]  0.85090352 -0.30481062 -0.99388865  0.74511316 -0.30481062 -0.85551998
##  [493]  0.37960774 -0.98803162 -0.30481062  0.74511316 -0.30481062 -0.30481062
##  [499] -0.30481062  0.77389068 -0.98803162  0.77389068 -0.30481062 -0.99975517
##  [505] -0.17607562  0.85090352  0.77389068  0.56610764 -0.86455145 -0.38778164
##  [511]  0.89399666 -0.71487643 -0.99975517  0.82682868  0.82682868 -0.30481062
##  [517] -0.50636564 -0.95375265  0.95105465  0.85090352  0.16735570  0.62298863
##  [523]  0.43616476 -0.85551998 -0.26237485  0.91294525 -0.50636564  0.56610764
##  [529] -0.26237485  0.99287265 -0.89792768  0.92681851  0.92681851  0.08836869
##  [535]  0.74511316  0.77389068  0.77389068 -0.89792768  0.92681851  0.74511316
##  [541]  0.77389068 -0.76825466  0.96836446 -0.98514626 -0.95375265 -0.11478481
##  [547]  0.85090352 -0.30481062  0.89399666  0.89399666  0.77389068  0.77389068
##  [553] -0.04424268  0.94543533 -0.50636564 -0.38778164 -0.38778164 -0.17607562
##  [559] -0.92345845  0.82682868  0.82682868 -0.38778164 -0.04424268 -0.17607562
##  [565] -0.89792768 -0.89792768 -0.30481062  0.85090352  0.77389068 -0.26237485
##  [571] -0.26237485 -0.26237485 -0.26237485 -0.26237485 -0.26237485 -0.38778164
##  [577] -0.99388865 -0.38778164 -0.50636564  0.89399666  0.10598751 -0.04424268
##  [583] -0.71487643 -0.71487643  0.58061118 -0.99388865 -0.50636564  0.77389068
##  [589] -0.50636564 -0.50636564  0.58061118 -0.50636564  0.85090352 -0.30481062
##  [595] -0.38778164  0.82682868  0.89399666 -0.04424268 -0.99975517 -0.38778164
##  [601]  0.68326171  0.85090352 -0.30481062  0.85090352  0.82682868 -0.17607562
##  [607] -0.15862267  0.92002604 -0.26237485 -0.38778164 -0.26237485 -0.38778164
##  [613] -0.26237485 -0.38778164  0.56610764  0.23666139 -0.26237485 -0.73918070
##  [619] -0.99388865  0.85090352 -0.38778164 -0.99975517  0.77389068 -0.17607562
##  [625]  0.82682868 -0.85551998 -0.30481062 -0.04424268  0.62298863  0.62298863
##  [631] -0.38778164 -0.17607562 -0.97053528 -0.26237485 -0.38778164 -0.97053528
##  [637]  0.58061118 -0.38778164  0.85090352 -0.99975517 -0.38778164 -0.98803162
##  [643]  0.74511316 -0.30481062  0.74511316 -0.30481062  0.85090352  0.77389068
##  [649]  0.77389068  0.77389068 -0.26237485 -0.30481062  0.68326171  0.77389068
##  [655]  0.77389068 -0.97053528 -0.97053528 -0.97053528 -0.97053528 -0.38778164
##  [661] -0.26237485  0.77389068 -0.26237485  0.82682868  0.25382336  0.29636858
##  [667]  0.29636858  0.99287265 -0.55878905 -0.98514626 -0.99975517 -0.38778164
##  [673] -0.26237485 -0.99388865  0.74511316 -0.30481062 -0.99975517 -0.38778164
##  [679]  0.85090352 -0.30481062  0.77389068  0.85090352  0.82682868 -0.04424268
##  [685] -0.73918070 -0.38778164 -0.99177885  0.67022918  0.95105465 -0.30481062
##  [691] -0.99388865 -0.99975517 -0.26237485  0.77389068 -0.11478481  0.78498039
##  [697] -0.99975517 -0.50636564  0.99779728 -0.26237485  0.77389068  0.01770193
##  [703] -0.98514626  0.74511316 -0.30481062 -0.30481062 -0.42818267  0.82682868
##  [709] -0.17607562 -0.99975517 -0.38778164 -0.26237485 -0.30481062 -0.30481062
##  [715]  0.90178835 -0.02655115  0.56610764 -0.99975517  0.68326171 -0.99388865
##  [721] -0.26237485 -0.99388865  0.81674261  0.81674261  0.85090352  0.82682868
##  [727]  0.99952016  0.63673801  0.86006941  0.85090352  0.82682868  0.68326171
##  [733]  0.77389068 -0.50636564  0.77389068 -0.04424268 -0.17607562  0.99287265
##  [739]  0.98662759  0.25382336 -0.77946607 -0.99975517 -0.17607562  0.10598751
##  [745]  0.10598751  0.10598751 -0.44411267 -0.44411267 -0.44411267 -0.44411267
##  [751] -0.50636564 -0.50636564  0.86006941  0.86006941 -0.61604046 -0.61604046
##  [757] -0.61604046  0.10598751  0.10598751 -0.50636564 -0.50636564  0.95105465
##  [763] -0.52155100 -0.96611777  0.03539830  0.74511316  0.63673801 -0.38778164
##  [769] -0.15862267 -0.55878905  0.25382336  0.25382336  0.29636858 -0.17607562
##  [775]  0.85090352 -0.73918070  0.51397846  0.29636858  0.85090352 -0.99388865
##  [781] -0.73918070 -0.92345845  0.01770193 -0.55878905  0.51397846 -0.02655115
##  [787] -0.45990349 -0.85551998  0.68326171 -0.38778164 -0.73918070 -0.98514626
##  [793] -0.98514626  0.85090352  0.63673801 -0.30481062 -0.30481062  0.51397846
##  [799]  0.45202579 -0.73918070  0.31322878  0.39592515 -0.92345845 -0.91652155
##  [805]  0.25382336 -0.26237485  0.82682868 -0.26237485  0.95105465  0.01770193
##  [811] -0.73918070  0.99287265  0.31322878  0.99952016 -0.45990349  0.68326171
##  [817]  0.51397846 -0.85551998 -0.26237485  0.85090352 -0.89792768  0.89399666
##  [823]  0.43616476 -0.83177474 -0.17607562 -0.95375265  0.01770193 -0.55878905
##  [829]  0.63673801  0.82682868 -0.99975517 -0.38778164 -0.17607562 -0.99975517
##  [835]  0.68326171  0.74511316 -0.17607562  0.32999083  0.32999083  0.92681851
##  [841] -0.55878905  0.69605849 -0.26237485 -0.26237485 -0.99388865 -0.99388865
##  [847] -0.99388865 -0.89792768  0.51397846  0.51397846  0.85090352  0.82682868
##  [853]  0.68326171 -0.26237485 -0.30481062 -0.99388865 -0.42818267 -0.99975517
##  [859] -0.99388865 -0.76825466  0.03539830  0.12357312  0.43616476  0.99952016
##  [865]  0.12357312  0.37960774 -0.38778164 -0.38778164 -0.38778164 -0.38778164
##  [871]  0.74511316 -0.30481062  0.85090352  0.85090352 -0.38778164 -0.17607562
##  [877] -0.38778164  0.85090352  0.85090352 -0.26237485 -0.26237485  0.77389068
##  [883] -0.50636564  0.29636858 -0.89792768  0.74511316  0.77389068  0.74511316
##  [889] -0.30481062 -0.76825466 -0.89792768  0.77389068  0.58061118 -0.91652155
##  [895]  0.98662759  0.25382336  0.67022918  0.89399666 -0.50636564 -0.13235175
##  [901] -0.38778164 -0.99975517 -0.17607562 -0.99975517  0.68326171  0.68326171
##  [907] -0.30481062 -0.30481062  0.82682868 -0.30481062  0.82682868 -0.99975517
##  [913] -0.89792768  0.51397846  0.77389068  0.85090352 -0.99975517 -0.38778164
##  [919]  0.77389068  0.77389068  0.77389068  0.77389068 -0.83177474 -0.83177474
##  [925] -0.94251445 -0.94251445  0.81674261  0.18478174  0.95105465  0.96836446
##  [931]  0.37960774  0.63673801  0.05305349  0.37960774  0.37960774  0.37960774
##  [937]  0.37960774 -0.99388865  0.89399666 -0.85551998 -0.67677196 -0.96611777
##  [943]  0.39592515  0.03539830  0.90178835  0.08836869 -0.26237485  0.77389068
##  [949] -0.50636564 -0.26237485  0.82682868 -0.99388865 -0.26237485  0.82682868
##  [955]  0.77389068  0.77389068  0.58061118  0.29636858 -0.89792768 -0.57338187
##  [961] -0.13235175 -0.26237485 -0.30481062  0.74511316  0.77389068  0.74511316
##  [967] -0.30481062 -0.91652155  0.25382336 -0.26237485  0.89399666  0.63673801
##  [973] -0.11478481 -0.98803162 -0.99388865 -0.04424268  0.74511316  0.77389068
##  [979] -0.04424268  0.98662759  0.25382336  0.77389068 -0.15862267 -0.96611777
##  [985]  0.74511316 -0.38778164 -0.38778164 -0.26237485 -0.50636564 -0.26237485
##  [991] -0.99388865  0.74511316 -0.30481062 -0.91652155  0.43616476  0.43616476
##  [997]  0.85090352  0.82682868  0.68326171 -0.94828214  0.77389068 -0.30481062
## [1003] -0.73918070 -0.99388865  0.99287265  0.85090352  0.82682868  0.82682868
## [1009] -0.76825466 -0.98803162  0.77389068 -0.50636564 -0.38778164 -0.38778164
## [1015] -0.30481062  0.77389068  0.99287265  0.99287265  0.25382336  0.49871315
## [1021]  0.89399666  0.89399666  0.89399666  0.89399666  0.77389068  0.27090579
## [1027] -0.89792768  0.03539830 -0.77946607 -0.77946607 -0.77946607 -0.77946607
## [1033]  0.98023966 -0.50639163 -0.30481062 -0.50636564 -0.50636564 -0.97053528
## [1039] -0.99388865 -0.87329730 -0.50636564 -0.50636564 -0.50636564 -0.50636564
## [1045] -0.50636564
cos(head(d2$HP))
## [1]  0.5253220 -0.9524130 -0.1103872 -0.1103872  0.2666429  0.1191801
factorial(head(d2$HP))
## [1]  1.196222e+56  8.320987e+81 7.156946e+118 7.156946e+118  2.039788e+46
## [6]  2.350561e+78
abs(head(d2$HP))
## [1] 45 60 80 80 39 58
sqrt(head(d2$HP))
## [1] 6.708204 7.745967 8.944272 8.944272 6.244998 7.615773
tan(head(d2$HP))
## [1] 1.6197752 0.3200404 9.0036549 9.0036549 3.6145544 8.3308569
asin(head(d2$HP))
## Warning in asin(head(d2$HP)): NaNs produced
## [1] NaN NaN NaN NaN NaN NaN

Viewing Previously Loaded Named-Objects.

ls()
## [1] "d.mat" "d2"    "dens"  "md"
ls(pattern = 'v')
## character(0)
ls(pattern = 'da')
## character(0)
var(head(d2$HP))
## [1] 293.8667
median(d2$HP)
## [1] 68
mode(d2$HP)
## [1] "numeric"

Selecting and Displaying Parts of a Object.

head(d2[1])
head(d2[1:4])
head(d2[-1])
head(d2[d2>7])
## [1] "Bulbasaur"     "Ivysaur"       "Venusaur"      "Mega Venusaur"
## [5] "Charmander"    "Charmeleon"
head(d2[d2<5 | d2>10],n=10)
##  [1] "Bulbasaur"        "Ivysaur"          "Venusaur"         "Mega Venusaur"   
##  [5] "Charmander"       "Charmeleon"       "Charizard"        "Mega Charizard"  
##  [9] "Mega Charizard X" "Squirtle"
head(d2[d2<5 | d2>10])
## [1] "Bulbasaur"     "Ivysaur"       "Venusaur"      "Mega Venusaur"
## [5] "Charmander"    "Charmeleon"
d2[100,8]
## [1] 90
d2[150,1:6]

Sorting and Rearranging a Object.

sort(head(d2$HP))
## [1] 39 45 58 60 80 80
sort(head(d2$HP), decreasing = TRUE)
## [1] 80 80 60 58 45 39
sort(head(d2$HP),na.last = NA)
## [1] 39 45 58 60 80 80
sort(head(d2$HP),na.last = TRUE)
## [1] 39 45 58 60 80 80
sort(head(d2$HP),na.last = FALSE)
## [1] 39 45 58 60 80 80
order(d2$HP)
##    [1]  352   64   65  167  218  263  417  427  526   82  109  900  961  339
##   [15] 1026   25   26  121  123  130  152  156  180  238  359  370  494  501
##   [29]  642  974 1010  350   31   33   60   66   67  113  127  178  207  221
##   [43]  234  275  289  318  440  707  857  687  485   49   50  320  321  340
##   [57]  666  667  773  778  884  958    5  201   17   21   29   55   68   69
##   [71]   75   79   83   92   94   95  132  143  158  211  213  223  269  306
##   [85]  329  332  335  337  343  373  411  428  449  480  490  496  535  540
##   [99]  643  645  675  704  766  836  871  886  888  964  966  977  985  992
##  [113]  191  607  769  983  804  894  968  994  409  444  824  923  924   10
##  [127]  424  474  702  783  810  827    1   14   18   34   57  124  154  198
##  [141]  277  280  291  292  293  310  324  383  395  400  415  445  487  506
##  [155]  520  547  568  593  602  604  620  639  647  679  682  725  730  775
##  [169]  779  794  820  851  873  874  878  879  916  997 1006   44  715  945
##  [183]  862  865  171  249  542  860  890 1009  518  545  826   15   37   38
##  [197]   70   73   89  100  101  110  122  137  140  141  159  204  219  252
##  [211]  271  290  299  307  314  325  327  358  360  362  363  364  365  366
##  [225]  380  396  398  407  434  467  468  469  470  525  529  570  571  572
##  [239]  573  574  575  609  611  613  617  634  651  661  663  673  693  700
##  [253]  712  721  806  808  819  843  844  854  880  881  947  950  953  962
##  [267]  970  988  990  688  897  111  112  739  895  980  477  802  943  669
##  [281]  770  784  828  841   27   41   77   84   85   96   97  131  153  172
##  [295]  212  222  225  235  237  241  266  288  441  442  471  481  504  513
##  [309]  599  622  640  671  677  692  697  710  718  742  831  834  858  902
##  [323]  904  912  917  763  523  823  863  995  996    6  202  530  668  738
##  [337]  812 1005 1017 1018   11  483  728  767  795  829  932  972    2   16
##  [351]   32   35   36   58   61   62  114  125  126  128  133  134  138  139
##  [365]  157  181  199  209  245  248  258  267  270  274  311  326  328  330
##  [379]  336  338  345  346  347  367  371  372  376  377  388  392  394  412
##  [393]  423  450  488  491  495  497  498  499  503  516  548  567  594  603
##  [407]  627  644  646  652  676  680  690  705  706  713  714  796  797  855
##  [421]  872  889  907  908  910  963  967  993 1002 1015 1035   45  192  351
##  [435]  764  942  984  618  685  776  781  791  800  811 1003   22  410  521
##  [449]  353  425  426  475  478  608   19   20   30   71   72   76   80   90
##  [463]  102  103  115  144  145  149  161  162  163  164  165  173  175  176
##  [477]  177  205  224  240  243  255  260  273  279  378  379  431  432  446
##  [491]  514  515  560  561  596  605  625  664  683  708  726  731  807  830
##  [505]  852  909  911  951  954  998 1007 1008  413  716  786  492  524  626
##  [519]  788  818  940  341  342  531  538  565  566  821  848  885  891  913
##  [533]  959 1027  546  695  973   42   47   63   86  160  179  214  226  230
##  [547]  232  251  261  262  300  308  309  315  319  333  344  361  368  369
##  [561]  374  375  381  384  385  389  390  391  399  419  420  421  422  437
##  [575]  500  502  507  536  537  541  551  552  569  588  623  648  649  650
##  [589]  654  655  662  681  694  701  733  735  882  887  892  915  919  920
##  [603]  921  922  948  955  956  965  978  982 1001 1011 1016 1025  519  689
##  [617]  762  809  929  356  665  740  771  772  805  896  969  981 1019   51
##  [631]   52  287  403  404  941  544  670  703  792  793   28   39   40   56
##  [645]   59  166  216  229  236  239  253  256  257  276  281  282  283  401
##  [659]  402  416  430  472  510  556  557  562  576  578  595  600  610  612
##  [673]  614  621  631  635  638  641  660  672  678  686  711  768  790  832
##  [687]  867  868  869  870  875  877  901  918  986  987 1013 1014  476  508
##  [701]  528  615  717  486  727  814  864    7    8    9  203  322  323  777
##  [715]  785  798  817  849  850  914   12   13  484  747  748  749  750    3
##  [729]    4   74   87   91   93   98   99  117  118  146  155  182  183  200
##  [743]  264  265  312  313  331  348  393  397  435  436  451  452  453  454
##  [757]  455  456  457  458  459  489  577  586  619  674  691  720  722  780
##  [771]  845  846  847  856  859  938  952  975  991 1004 1039   46  801  813
##  [785]   23   24  543  930  354  479  129  206  208  215  278  286  482  505
##  [799]  558  564  606  624  632  709  737  743  774  825  833  837  876  903
##  [813]  414  559  782  803  765  861  944 1028  729  753  754   43   78   81
##  [827]   88  104  105  116  142  185  186  187  188  189  190  220  227  228
##  [841]  233  259  268  284  285  296  334  405  406  438  511  549  550  580
##  [855]  597  822  898  939  971 1021 1022 1023 1024  193  581  744  745  746
##  [869]  758  759  741 1029 1030 1031 1032 1000   48  106  107  108  135  136
##  [883]  168  169  242  244  246  247  294  418  433  447  448  473  601  653
##  [897]  719  732  789  816  835  853  905  906  999  493  866  931  934  935
##  [911]  936  937  960  429  197  210  231  254  272  298  301  302  305  316
##  [925]  317  382  443  460  461  462  463  466  517  527  555  579  587  589
##  [939]  590  592  698  734  751  752  760  761  883  899  949  989 1012 1036
##  [953] 1037 1041 1042 1043 1044 1045  799  522  629  630  355  119  120  147
##  [967]  150  151  464  465  633  636  656  657  658  659 1038  194  195  196
##  [981]  303  304  928  532  533  539  840  723  724  927  408  439  553  563
##  [995]  582  598  628  684  736  976  979  509  696   53  297  554  616  585
## [1009]  591  637  893  957 1020  787  815  217  755  756  757  838  839  170
## [1023]  174  386  534  946  925  926   54 1033  357  349  512  583  584  184
## [1037]  699  387  250 1040  842  933  148  295 1034
rank(d2)
##       Name      Total         HP     Attack    Defence  Sp_attack Sp_defence 
##     7398.0     7687.0     8298.0     7927.0     7423.0     7424.0     7421.0 
##      Speed       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     7814.0     7815.0     8202.0     8317.0     7379.0     7806.0     7415.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     7949.0     7402.0     8320.0     7699.0     7367.0     7805.0     8036.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     8035.0     8034.0     7889.0     8081.0     7899.0     8080.0     7898.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     8193.0     7562.0     7540.0     7336.0     8039.0     7890.0     8075.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     7896.0     8116.0     7909.0     8117.0     7910.0     7988.0     7989.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     7986.0     7987.0     7990.0     7985.0     7443.0     7442.0     8313.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     7928.0     7993.0     7884.0     7690.0     8327.0     8358.0     7613.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     8003.0     7611.0     8305.0     8020.0     8021.0     8297.0     8296.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     7499.0     7825.0     7526.0     7826.0     7945.0     7871.0     7872.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     8027.0     7888.0     8063.0     7615.0     7779.0     8060.0     7639.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     7337.0     8049.0     8050.0     8051.0     7317.0     7698.0     7324.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     7798.0     7763.0     7762.0     7761.0     7371.0     8321.0     8302.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     8242.0     8243.0     7600.0     7836.0     7630.0     7843.0     7616.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     7839.0     8053.0     7892.0     8079.0     7897.0     8177.0     7918.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     8175.0     7915.0     7916.0     7770.0     7771.0     7561.0     7831.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     7502.0     7501.0     8133.0     7492.0     7633.0     7845.0     7976.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     7880.0     8144.0     7446.0     7596.0     7653.0     7599.0     7835.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     8006.0     7522.0     7678.0     7715.0     7706.0     8311.0     7545.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     7557.0     7558.0     7830.0     7475.0     7786.0     7866.0     7665.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     7664.0     7737.0     7712.0     8322.0     7929.0     8094.0     8093.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     7420.0     8234.0     7700.0     7852.0     7673.0     8129.0     7614.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     8130.0     8209.0     8208.0     7971.0     7879.0     8128.0     7695.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     7542.0     7768.0     8044.0     7891.0     8240.0     7767.0     7646.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     7847.0     7727.0     7500.0     7538.0     7827.0     8294.0     7692.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     7572.0     8056.0     8004.0     8005.0     7696.0     7697.0     7321.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     7796.0     8185.0     7348.0     7801.0     8350.0     7936.0     7965.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     7877.0     7515.0     7510.0     7511.0     7951.0     7874.0     7875.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     7950.0     7431.0     7364.0     7940.0     7479.0     8071.0     8280.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     8261.0     7471.0     7565.0     8135.0     7589.0     7671.0     7995.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     7735.0     7734.0     8197.0     7343.0     7470.0     7434.0     7726.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     8033.0     7444.0     7679.0     8254.0     8255.0     7982.0     8338.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     7784.0     7569.0     7331.0     7800.0     7370.0     7785.0     7354.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     8217.0     8048.0     7672.0     8166.0     7694.0     7323.0     8220.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     8219.0     8343.0     8334.0     8070.0     7553.0     8285.0     7979.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     8176.0     7917.0     7963.0     8287.0     8331.0     7603.0     8043.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     7581.0     7527.0     7609.0     8210.0     7919.0     8188.0     7628.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     8073.0     8123.0     7912.0     8152.0     7660.0     7848.0     8182.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     8241.0     8288.0     8178.0     7765.0     8228.0     8041.0     7456.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     7816.0     8090.0     8002.0     7489.0     7780.0     8164.0     7675.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     7674.0     7850.0     7705.0     8029.0     7503.0     8058.0     8204.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     8180.0     8283.0     7666.0     8181.0     7546.0     7764.0     7957.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     7382.0     8076.0     7551.0     8218.0     7729.0     8065.0     8281.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     7925.0     7752.0     7667.0     7416.0     8269.0     7638.0     8122.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     7911.0     8256.0     7451.0     7380.0     7807.0     7974.0     7788.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     8225.0     7921.0     8054.0     7954.0     8355.0     7937.0     7742.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     7860.0     8336.0     8155.0     7366.0     7413.0     7534.0     7748.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     7746.0     7751.0     8132.0     8000.0     8148.0     8232.0     8227.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     8329.0     8025.0     8077.0     7707.0     7595.0     7834.0     8221.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     7789.0     8151.0     7391.0     8173.0     8303.0     8172.0     7992.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     7994.0     8142.0     8326.0     7749.0     7559.0     7773.0     7649.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     7355.0     7998.0     8167.0     7488.0     8109.0     7907.0     7790.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     7867.0     7346.0     7723.0     7322.0     7797.0     7792.0     7791.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     7868.0     7544.0     7778.0     7865.0     8046.0     7962.0     8308.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     7680.0     8103.0     7642.0     8224.0     7412.0     8140.0     7913.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     8314.0     8315.0     7999.0     7407.0     7810.0     8257.0     8200.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     7641.0     8198.0     8268.0     8300.0     7578.0     7404.0     7405.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     8222.0     7327.0     7799.0     8349.0     8138.0     7755.0     8192.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     7360.0     8325.0     7455.0     7467.0     7357.0     7441.0     7739.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     7464.0     7332.0     7344.0     7563.0     7956.0     7414.0     7811.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     7812.5     7812.5     7703.0     8153.0     7358.0     7803.0     7533.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     7531.0     8271.0     7433.0     7318.0     7794.0     8337.0     8186.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     7606.0     7838.0     8196.0     8131.0     8316.0     7438.0     7676.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     7622.0     8089.0     7757.0     7356.0     8143.0     8110.0     7908.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     7369.0     7948.0     7947.0     7873.0     8087.0     8083.0     8088.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     7730.0     7858.0     7731.0     7859.0     7721.0     7854.0     7637.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     7846.0     8082.0     7900.0     7691.0     7491.0     7821.0     7822.5 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     7822.5     8276.0     7636.0     8260.0     7432.0     7966.0     7684.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     8045.0     8061.0     7550.0     8207.0     8206.0     8205.0     7375.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     7374.0     7716.0     7717.0     8150.0     7758.0     7759.0     7396.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     8104.0     7466.0     8078.0     8147.0     7363.0     7401.0     8335.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     7931.0     7932.0     7970.0     7450.0     8299.0     8011.0     7397.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     7575.0     7427.0     7426.0     8145.0     7597.0     7329.0     7519.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     7518.0     7399.0     7747.0     7861.0     7964.0     7668.0     7607.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     8067.0     7435.0     8216.0     8170.0     7394.0     7393.0     7386.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     7958.0     7648.0     7425.0     8199.0     7601.0     7590.0     7594.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     7833.0     7977.0     8098.0     7750.0     7862.0     7662.0     7663.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     8168.0     7514.0     7469.0     8265.0     7410.0     7568.0     7753.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     7781.0     8187.0     7316.0     7793.0     8319.0     7772.0     7736.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     8095.0     8235.0     7543.0     7769.0     8253.0     8344.0     7732.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     7605.0     7610.0     7775.0     8057.0     7591.0     7832.0     8062.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     7532.0     7586.0     8105.0     7902.0     7904.5     7904.5     7904.5 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     7904.5     8290.0     7946.0     7353.0     7496.0     8012.0     7657.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     8086.0     7604.0     7837.0     7468.0     8032.0     7776.0     7480.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     8141.0     7914.0     7338.0     8301.0     8183.0     8137.0     8136.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     8244.0     8038.0     7548.0     8010.0     7493.0     8113.0     8023.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     8318.0     7741.0     7661.0     8213.0     8066.0     7738.0     8018.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     8159.0     8019.0     8160.0     8017.0     8158.0     7978.0     7980.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     8037.0     8267.0     8286.0     7383.0     8352.0     8100.0     7384.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     7602.0     8332.0     8230.0     7520.0     7556.0     7349.0     7802.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     8250.0     7644.0     7453.0     8277.0     8014.0     8134.0     8247.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     8119.0     8139.0     8223.0     7733.0     8295.0     8324.0     8124.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     7461.0     8323.0     8028.0     7740.0     7362.0     7804.0     8115.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     7718.0     7719.0     7483.0     7820.0     7481.0     7817.0     7818.5 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     7818.5     7782.0     7535.0     7472.0     8127.0     8126.0     8154.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     8341.0     7933.0     7449.0     8251.0     7411.0     7339.0     7340.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     8272.0     7593.0     8357.0     8356.0     7960.0     7436.0     7624.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     7626.0     7625.0     8191.0     7528.0     8092.0     7525.0     8226.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     8292.0     8291.0     8293.0     7486.0     8120.0     7549.0     7701.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     7552.0     7580.0     7330.0     7583.0     7689.0     7326.0     7693.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     7592.0     7566.0     7567.0     7710.0     7708.0     7711.0     8278.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     7536.0     7537.0     7547.0     7368.0     7745.0     7724.0     7419.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     7352.0     7582.0     7655.0     7474.0     7365.0     7473.0     8146.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     7319.0     8215.0     7920.0     7952.0     7953.0     7523.0     7617.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     7619.0     8024.0     7377.0     7387.0     8107.0     7390.0     8312.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     7777.0     7656.0     7530.0     7487.0     8359.0     7677.0     7728.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     8310.0     7448.0     8245.0     8306.0     8258.0     7923.0     8248.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     7922.0     8091.0     8353.0     7725.0     7857.0     7722.0     7855.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     7856.0     7704.0     7853.0     7942.0     7869.0     7598.0     7429.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     8072.0     7428.0     7564.0     7389.0     7490.0     7584.0     7585.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     7632.0     7844.0     7400.0     7498.0     7574.0     7573.0     8233.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     8121.0     8195.0     8307.0     7743.0     8068.0     7570.0     7576.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     7577.0     8165.0     7612.0     8015.0     8016.0     7588.0     7554.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     7944.0     7870.0     7669.0     7505.0     7320.0     7795.0     8201.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     7345.0     8229.0     8179.0     7685.0     7774.0     7376.0     7359.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     8169.0     7508.0     7439.0     7440.0     7659.0     7658.0     8284.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     8282.0     7328.0     7350.0     8231.0     7654.0     7485.0     7408.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     7621.0     8174.0     7620.0     7709.0     8030.0     8270.0     8064.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     7893.0     7894.5     7894.5     7627.0     7840.0     7841.5     7841.5 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     7372.0     7351.0     7996.0     7997.0     8339.0     8346.0     8360.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     7938.0     7939.0     7497.0     7824.0     7670.0     7849.0     8309.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     8106.0     7482.0     7484.0     7744.0     8259.0     7682.0     8055.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     7392.0     8059.0     8040.0     8273.0     8262.0     8345.0     7643.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     7640.0     7422.0     8304.0     7463.0     7462.0     8009.0     7885.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     7886.5     7886.5     7478.0     8096.0     8099.0     7901.0     7760.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     7863.0     7864.0     8330.0     7930.0     7783.0     8263.0     7973.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     7975.0     7494.0     7335.0     7579.0     7756.0     7967.0     8149.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     8111.0     8112.0     8214.0     7373.0     7388.0     8211.0     8274.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     7452.0     8007.0     8022.0     8328.0     7618.0     8118.0     8013.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     8069.0     8279.0     8157.0     7961.0     7876.0     7713.0     8275.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     8252.0     7959.0     7395.0     7513.0     7495.0     7688.0     7647.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     7714.0     8238.0     8239.0     8236.0     8237.0     7460.0     7459.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     8190.0     7754.0     7991.0     7403.0     8031.0     8340.0     7417.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     7702.0     7645.0     7983.0     7881.0     7882.5     7882.5     7766.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     7787.0     8047.0     7981.0     8203.0     7378.0     8354.0     7943.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     7941.0     7635.0     8249.0     8097.0     8125.0     8074.0     7437.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     8189.0     7521.0     7686.0     8171.0     7631.0     8102.0     7458.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     7457.0     7381.0     7504.0     8008.0     7984.0     8246.0     7623.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     7541.0     8333.0     7524.0     7430.0     7516.0     8342.0     7385.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     8101.0     7409.0     7447.0     7334.0     7571.0     7333.0     8156.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     8114.0     7465.0     7347.0     7361.0     8264.0     8266.0     7924.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     8163.0     7418.0     7445.0     7629.0     8161.0     8052.0     7650.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     7652.0     7651.0     7681.0     7968.0     7634.0     8001.0     8026.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     7477.0     8162.0     7972.0     8108.0     7955.0     7325.0     7560.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     8042.0     8184.0     7587.0     8212.0     7539.0     7828.0     7683.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     7851.0     7969.0     7878.0     7476.0     7454.0     7507.0     7342.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     7506.0     7341.0     7529.0     7517.0     7512.0     7509.0     8347.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     7934.0     8348.0     7935.0     7555.0     7829.0     7720.0     8289.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     7926.0     8351.0     8085.0     8084.0     7608.0     8194.0     7406.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     7808.0     7809.0      595.0     1273.5     2525.0     3535.5      555.5 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     1273.5     2590.0     3580.5     3580.5      582.5     1273.5     2577.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     3577.5       54.0      121.0      973.5       54.0      121.0      973.5 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     2007.0      228.0      700.0     1841.5     2967.0      230.0      230.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     1324.5     1324.5      238.5     1472.0      297.5     1475.5      616.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     1419.5     1930.5     1930.5      512.5      512.5     1719.5     1719.5 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##      265.5      888.0     2420.5      262.0      888.0     2420.5      623.5 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     1916.0      331.5      331.5     2420.5     2420.5      258.0     1427.5 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##      162.5     1735.5      616.0      973.5     1978.0      293.5     1273.5 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##      539.0     1719.5      244.5      244.5     1384.5     1384.5      315.5 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##      315.5      315.5     1466.0     1466.0      616.0     2395.0      539.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     1735.5      851.0     2900.5      512.5      950.5     2450.5      572.5 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     1256.0     2395.0     3446.5      539.0     1273.5     2420.5      512.5 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##      966.5     1978.0      667.5     2464.0      512.5      512.5      966.5 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##      966.5     2007.0     2007.0     1313.5     1313.5     2395.0     2395.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##      587.5      587.5     1978.0     3063.0     1978.0      629.5     1780.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##      914.5      914.5      572.5     1809.0      629.5     1829.0      629.5 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##      629.5     2395.0     2395.0      539.0     2525.0      572.5     1273.5 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     2395.0     3446.5      950.5      635.5     1916.0      629.5     1829.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##      653.5     1978.0      629.5     2577.0     2577.0      616.0     1384.5 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     1384.5     1735.5     1735.5      950.5      687.0     1978.0     1978.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##      695.0     1930.5     1719.5     1427.5     1978.0     3063.0      327.5 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     1466.0      616.0     1719.5      687.0     2508.0     1765.0     1765.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     2395.0     1735.5     1978.0     2007.0     2395.0     3446.5     1978.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##      116.5     2627.0     3608.5     2597.5      297.5      629.5     1427.5 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     2525.0     2525.0     2525.0      973.5      859.0     2007.0      859.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     2007.0     2464.0     3498.0     2627.0     3025.5     3025.5     3025.5 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     3025.5     3025.5     3025.5      512.5     1366.0     3446.5     4023.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     4839.0     4839.0     3446.5      595.0     1273.5     2525.0      555.5 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     1273.5     2590.0      582.5     1273.5     2577.0      129.0     1327.5 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##      238.5     1725.0      244.5      966.5      223.0     1256.0     2597.5 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##      653.5     1765.0      121.0      130.0      125.5      162.5     1273.5 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##      616.0     1809.0      284.5      888.0     2450.5     3496.5     1978.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##      223.0     1366.0     1313.5     2395.0      223.0      687.0     1765.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##      879.5       48.5     1384.5      966.5      125.5     1419.5     2525.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     2525.0     1273.5     1978.0     1978.0     1427.5      673.0     1273.5 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     1735.5      315.5     1780.0     1327.5     1419.5     2450.5     3496.5 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##      512.5     1719.5     1466.0     2395.0     3446.5     2420.5     2395.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     3446.5     1419.5      653.5     2395.0      223.0     1419.5      223.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     1719.5     1313.5     1313.5      512.5     1900.5      653.5     1930.5 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     1780.0      653.5     2395.0     3446.5     2627.0      653.5     2395.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     2464.0     1780.0      223.0      125.5     1735.5      539.0      879.5 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##      888.0     1978.0     2627.0     3025.5     3025.5     3025.5      512.5 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     1313.5     3446.5     4400.0     4023.0     4023.0     3446.5      572.5 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     1273.5     2577.0     3577.5      572.5     1273.5     2577.0     3577.5 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##      572.5     1273.5     2597.5     3582.0      134.5     1366.0      155.5 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##      155.5     1366.0     1366.0       54.0      121.0      973.5      121.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##      950.5      134.5      687.0     1900.5      134.5      687.0     1900.5 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##      258.0     1735.5      258.0     1466.0       56.0      269.5     2470.5 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     3499.5      249.5     1728.0      327.5     1765.0      284.5     1466.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     3980.5      248.0     1742.0      147.0      155.5      879.5     1978.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##      148.5     1821.5       51.0      913.0      235.5     1256.0      943.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     1900.5      943.0     1900.5      653.5     1419.5     2577.0     3577.5 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##      284.5     1313.5     2450.5      327.5     1829.0     2966.0     1273.5 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     1273.5     1419.5     1419.5     1256.0      525.0     1786.0      539.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     1765.0     2925.5     1256.0     2395.0      539.0     1765.0     2925.5 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     1809.0      653.5     1809.0      879.5      315.5      687.0     2508.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##      667.5     1829.0      572.5     1978.0     3063.0     1743.5     1743.5 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     1765.0     1765.0      297.5     1787.5      551.0     1787.5      512.5 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     2395.0      859.0     2007.0      859.0     2007.0      116.5     2627.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     1366.0     1366.0     1366.0     1366.0     1466.0      327.5     1735.5 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     2900.5      327.5     1735.5     1765.0     1735.5     1780.0     2927.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##      235.5      512.5     1900.5     3025.5      315.5     1313.5     2577.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##      695.0     1930.5     1930.5     1930.5      653.5      512.5     1366.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     3446.5     4400.0      512.5     1366.0     3446.5     4400.0     3025.5 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     3025.5     3025.5     3446.5     4400.0     3446.5     4400.0     3980.5 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     4806.5     3980.5     4806.5     4023.0     4839.0     3446.5     3446.5 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     3446.5     3446.5     3446.5      595.0     1273.5     2525.0      555.5 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     1273.5     2590.0      582.5     1273.5     2577.0      162.5      687.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     1930.5      223.0     1313.5       52.0      946.5      240.0      885.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     2516.0      284.5     2464.0      851.0     2007.0      851.0     2007.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##      137.0     1379.5     1379.5     1379.5     1379.5      159.0     1821.5 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     1273.5      653.5     2007.0      265.5     1719.5      629.5     1829.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     1912.5      698.0     2019.5      851.0     1900.5     3025.5     2007.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     2420.5      572.5     1725.0      293.5      637.5     1841.5      512.5 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     2395.0      315.5      572.5      134.5     1321.5     1930.5      512.5 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     1313.5     3446.5     4400.0      966.5      293.5     2525.0     3535.5 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##      653.5     2525.0      653.5     2395.0      512.5     1978.0     1728.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##      653.5     1765.0      695.0      662.0     1994.5     3065.0     2450.5 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     2597.5     2464.0     2597.5     2597.5     2627.0     2627.0     2633.5 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     2464.0     2525.0     2525.0     2450.5     2577.0     2597.5     2470.5 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     3499.5     2525.0     2525.0     1900.5     1466.0     2508.0     2508.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     2508.0     2508.0     2508.0     3025.5     3025.5     3025.5     4023.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     4023.0     3446.5     3980.5     4023.0     4023.0     3446.5     1900.5 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     3446.5     3446.5     3446.5     3446.5     4472.0     3446.5      551.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     1324.5     2535.0      551.0     1329.5     2535.0      551.0     1324.5 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     2535.0      232.0     1366.0      265.5      908.5     2395.0      290.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     1474.0      592.0     2019.5      592.0     2019.5      592.0     2019.5 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##      321.5     1943.5      241.0      863.0     1946.5      327.5     2017.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##      284.5      966.5     2464.0      623.5     1384.5      635.5     2430.5 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     1473.0     2633.5      539.0     1273.5     2420.5      323.5      946.5 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     2432.0     1780.0     1780.0      572.5      943.0     2395.0      235.5 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##      879.5     1930.5      284.5     1900.5      284.5     1900.5     1765.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     1765.0      321.5      855.0     2472.0      587.5      587.5     1900.5 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     2627.0     1900.5     2627.0     1773.0      629.5     1930.5      698.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     1946.5     1978.0      527.5      527.5     1916.0      859.0     2007.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     1260.0     2928.0      637.5     1821.5      653.5     2450.5      512.5 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     1809.0      315.5      966.5     1978.0      315.5      908.5     1978.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##      539.0     1819.0      539.0      973.5     2597.5      667.5     1829.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     1388.0      587.5     2007.0      323.5     1775.5      667.5     1900.5 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     1809.0      597.0     1817.5      539.0     1948.0      512.5     1466.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     2508.0      265.5     1273.5     2464.0      667.5     1930.5      265.5 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##      908.5     2508.0      616.0     1313.5     2627.0      539.0     2420.5 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     2464.0      539.0     2007.0     1815.5     1815.5      851.0     2450.5 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     1930.5      527.5     1916.0      687.0     1978.0     1978.0      851.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     2450.5      908.5     2450.5     1919.5     1919.5      512.5     1366.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     3446.5      879.5     2897.0     3025.5     3025.5     3025.5     3025.5 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     3025.5     3025.5     3025.5     4023.0     4023.0     3446.5     3446.5 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     3947.0     4400.0     4400.0     3025.5     3025.5     3446.5     3446.5 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     3446.5      580.0     1273.5     2577.0      548.0     1287.0     2590.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##      582.5     1273.5     2577.0     3608.5      148.5     1377.0      269.5 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##      945.0     2022.0      116.5      128.0     1321.5      891.0     2428.5 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##      527.5      912.0     2899.0      851.0     2587.0      698.0     2007.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     1817.5      859.0     1784.5     1784.5      629.5     1475.5     2395.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     2395.0      692.5     1774.0      692.5     1900.5      297.5     1912.5 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##      547.0     2395.0      616.0     1994.5      653.5     2395.0      300.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     1911.0      883.5     2514.5      883.5     2514.5     2525.0     2395.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     1424.0     2395.0      512.5     1725.0     3446.5     1809.0      555.5 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     1821.5      667.5      667.5      667.5      667.5     1994.5     1994.5 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     1994.5     1994.5      530.5     2459.0      162.5     2597.5     4023.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     4023.0     3446.5     1941.0     4405.0     3446.5     4400.0     3446.5 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     4023.0     3446.5      616.0     1366.0     2577.0      616.0     1366.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     2577.0      616.0     1366.0     2577.0      244.5      859.0     1930.5 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##      230.0     1329.5      512.5     1256.0     2395.0      674.0     1840.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     1837.0     1837.0     1837.0     1837.0      530.5     1775.5      284.5 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##      284.5     1943.5     1943.5     1943.5       47.0     3534.0      539.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     2007.0      950.5     2395.0      249.5     1728.0      223.0     1900.5 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##      293.5     1273.5      616.0     1900.5      687.0     2395.0      125.5 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##      315.5     2450.5     1930.5     1978.0     1978.0      146.0     2577.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##      616.0     1900.5     1313.5     2590.0     2958.5     1466.0     2395.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     1900.5     1930.5     1427.5     1837.0     1829.0     1930.5     2469.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##      512.5     1366.0     3446.5     2958.5     2958.5     2958.5     2958.5 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##      116.5     1256.0     4023.0     4023.0     2958.5     2958.5     2958.5 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     2958.5     2958.5     2958.5     2958.5     3446.5     4023.0     4023.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     4757.0     3446.5     3446.5     1366.0     2627.0     2958.5     2958.5 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     3446.5      512.5     3446.5      572.5     1366.0     2577.0      572.5 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     1366.0     2577.0      572.5     1366.0     2577.0      265.5     1765.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##      162.5      888.0     2007.0       48.5      667.5     2420.5      162.5 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     1735.5      223.0     1765.0      258.0     1978.0      291.0     1930.5 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##      258.0     1978.0      155.5     1313.5     2450.5      235.5     1930.5 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     1930.5      587.5     2450.5     1829.0      284.5     1978.0      158.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     2412.5     2412.5      539.0     2525.0      572.5     1900.5      551.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     2430.5      244.5      908.5     2450.5      244.5      908.5     2450.5 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     2508.0     1466.0     2450.5     2428.5     2508.0     1916.0      258.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     2007.0     1809.0     1427.5       50.0     1829.0     1809.0     1809.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     1809.0     1829.0     1829.0     1431.5     1431.5      653.5     2395.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     2420.5     2420.5     2420.5     2420.5     2597.5      258.0     1313.5 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     3446.5     4472.0     3980.5     4472.0     3980.5     4056.0     6687.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##      950.5     2897.0     2897.0     3446.5     3025.5     3025.5     3025.5 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     3025.5     2395.0     4023.0     4023.0     1596.0     3244.5     5011.5 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     5011.5      958.0     2989.0     4822.5     4822.5     4822.5     1446.5 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     3051.0     4852.0     4852.0     1596.0     2200.5     3244.5     1114.5 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     1596.0     3766.5     3766.5     1114.5     3556.0     5213.5     5213.5 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##      416.5      416.5     2765.0     4638.0     1114.5     3766.5      774.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     3244.5      774.0     1596.0     3244.5     3244.5     2200.5     2200.5 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     4638.0     4638.0     2765.0     4226.0     5634.0     1751.0     3482.5 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     5170.0     4226.0     5927.0      928.5      928.5     4487.5     4487.5 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     6738.5     7147.0     1114.5     4638.0     1596.0     3244.5     4638.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##      774.0     3244.5     3244.5     4226.0       15.5       15.5      774.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##      774.0     1114.5     1114.5     2200.5     3766.5     3766.5     2200.5 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     5011.5     1114.5     3766.5     2765.0     5634.0     1114.5     3766.5 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     5634.0      192.0     1114.5     2765.0     2765.0     4226.0     5011.5 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     5634.0     2200.5     3766.5     5011.5     1114.5     5011.5     1114.5 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     1114.5     2765.0     2765.0     5011.5     5011.5     2200.5     2200.5 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     3766.5     3766.5     5634.0     5634.0     5927.0     5927.0     5927.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##      192.0     2200.5     2487.5     2487.5      774.0     3244.5     3766.5 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     5634.0     5011.5     5011.5     6448.5     6448.5      416.5     2200.5 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##      416.5     1596.0     3244.5     3244.5      774.0     3244.5     5343.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##      416.5     2765.0     1114.5     3244.5     3244.5     5927.0     5927.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     2200.5     3244.5     3244.5     2200.5     2200.5     5634.0     1114.5 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     3766.5     3766.5     5011.5     6448.5     7312.0     3766.5     6448.5 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     6448.5      416.5     2765.0     1596.0     5011.5      416.5     3244.5 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     1114.5     2200.5     4226.0     3766.5     3766.5     3766.5     3766.5 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     3766.5     4638.0       85.5     5927.0     5927.0     7031.5     1866.5 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     2765.0     3766.5     7031.5     3766.5     3766.5     3766.5      774.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     4226.0      416.5     3244.5     5011.5     5011.5     7254.0     5634.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     5634.0     5634.0     5634.0     5634.0     5634.0     1297.0     3482.5 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     5770.0     6509.0     6509.0     6509.0     6223.5     1596.0     3244.5 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     5011.5      958.0     2989.0     4822.5     2200.5     3766.5     5343.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##      774.0     5343.0     3244.5     6223.5     1114.5     2765.0     1114.5 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     4226.0     5343.0     4638.0     6940.5       85.5     2200.5     5634.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##      774.0     2765.0     1114.5     3766.5     2765.0     4226.0     5634.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     5634.0     4638.0     4226.0     6223.5     4226.0     5634.0      774.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     2765.0     4638.0     2765.0      416.5     4638.0     3766.5     2765.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     5927.0     3766.5     5927.0     3244.5     5927.0     5927.0     3244.5 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     1866.5     7296.5     4226.0     2200.5     4638.0     6223.5     3766.5 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     4638.0     4638.0     3244.5     5634.0     3766.5     4226.0     4226.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##       85.5     5011.5     5011.5     2765.0     3244.5     5634.0     1114.5 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     3244.5     2200.5     6223.5     3766.5     3244.5      774.0     4638.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     1596.0     5343.0     3766.5     1596.0     4638.0     4638.0     4638.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     5634.0     5634.0     5343.0     4487.5     2765.0      774.0     2200.5 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     1596.0     1596.0     1596.0     5927.0     7314.5     5634.0     6738.5 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     6223.5     2200.5     4226.0     6223.5     6223.5     6509.0     6509.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     6223.5     1114.5     2200.5     4226.0     4226.0     1596.0     3244.5 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     5011.5     5011.5     2200.5     4226.0     6223.5     6223.5      774.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     4226.0      928.5      928.5     4822.5     4822.5     1596.0     2200.5 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     3244.5     2200.5     3244.5     1114.5     3244.5     5011.5     1114.5 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     4226.0     5634.0     1114.5     3244.5     1114.5     3244.5      275.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##      928.5     3998.5     3998.5     1114.5     4226.0     3244.5     3244.5 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     3244.5     5011.5     7212.0      561.5     3482.5        1.0     3595.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     5233.5     6389.5     4452.0     7167.0     2200.5      416.5     2200.5 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     4226.0     2200.5     2200.5     2200.5     2200.5     2200.5     3244.5 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     4226.0     4226.0      416.5     3244.5     3244.5     1114.5     4226.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     4226.0     3244.5     3244.5     3766.5     3766.5     2200.5     4226.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     6223.5     1596.0     4226.0     4226.0     7031.5     7277.0     3244.5 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     4226.0     4226.0     4226.0     3244.5     5011.5     3244.5     1596.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     2200.5     5011.5     2200.5     4226.0     1596.0     4638.0     4638.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     4487.5     4487.5     5634.0     5634.0     2200.5     6618.5     1402.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     3556.0     1114.5     3244.5     3935.0     5458.0     1596.0     4638.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##       85.5     5927.0     4226.0     4226.0     4226.0     4226.0     3244.5 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     1446.5     3595.0     3595.0       85.5     1114.5     6092.5     4638.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     3766.5     3766.5     5927.0     2200.5     5011.5     5011.5     4226.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     5634.0     6618.5      774.0     2765.0     2765.0     6223.5     1402.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     1596.0     3766.5     5927.0     5927.0     1114.5     3244.5     5011.5 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     5011.5     5011.5     5011.5     5011.5     5011.5     5011.5     5011.5 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     5011.5     6223.5     6223.5     6223.5     6223.5     6448.5     6448.5 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     6223.5     2200.5     2200.5     2200.5     2200.5     2765.0     4638.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     5927.0     1446.5     3595.0     4767.5     2552.0     3595.0     5233.5 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     1114.5     2765.0     5343.0     3051.0     4852.0      898.5     4791.5 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     1596.0     3244.5     5011.5     1114.5     3244.5     3962.5     6051.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##      416.5     3244.5     1114.5     3244.5     3244.5     3244.5     4226.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##      416.5     4226.0     3244.5     2765.0     5343.0     1596.0     4226.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     4767.5     6673.5     4638.0     5634.0     7212.0     2765.0     3766.5 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     3766.5     3244.5     6223.5     1956.5     4419.5     1596.0     3556.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     6379.0     2940.0     3962.5     2200.5       85.5     6223.5     4767.5 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     2200.5     2989.0     3998.5     6543.0     6543.0     7104.0     1114.5 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     4226.0     4226.0     3998.5     6543.0     1114.5     4226.0     1866.5 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     5213.5     4510.5     1956.5     4044.0     1596.0     3244.5     5634.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     5634.0     4226.0     4226.0     6618.5     6738.5     6223.5     4638.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     4638.0     5343.0     5458.0     3766.5     3766.5     4638.0     6618.5 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     5343.0     3998.5     3998.5     3244.5     1596.0     4226.0     2200.5 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     2200.5     2200.5     2200.5     2200.5     2200.5     4638.0     5011.5 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     4638.0     6223.5     5634.0     5770.0     6618.5     7212.0     7212.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     6840.0     5011.5     6223.5     4226.0     6223.5     6223.5     6840.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     6223.5     1596.0     3244.5     4638.0     3766.5     5634.0     6618.5 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     2765.0     4638.0     5927.0     1596.0     3244.5     1596.0     3766.5 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     5343.0     1297.0     3595.0     2200.5     4638.0     2200.5     4638.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     2200.5     4638.0     4767.5     6781.0     2200.5     3517.0     5011.5 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     1596.0     4638.0     2765.0     4226.0     5343.0     3766.5     3962.5 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     3244.5     6618.5     6379.0     6379.0     4638.0     5343.0     6448.5 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     2200.5     4638.0     6448.5     6840.0     4638.0     1596.0     2765.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     4638.0      416.5     1114.5     3244.5     1114.5     3244.5     1596.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     4226.0     4226.0     4226.0     2200.5     3244.5     5927.0     4226.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     4226.0     6448.5     6448.5     6448.5     6448.5     4638.0     2200.5 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     4226.0     2200.5     3766.5     4452.0      928.5      928.5     2989.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     2612.0     4510.5     2765.0     4638.0     2200.5     5011.5     1114.5 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     3244.5     2765.0     4638.0     1596.0     3244.5     4226.0     1596.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     3766.5     6618.5     3517.0     4638.0      870.0     2437.5     4419.5 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     3244.5     5011.5     2765.0     2200.5     4226.0     4044.0     6697.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     2765.0     6223.5     7266.5     2200.5     4226.0     1446.5     4510.5 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     1114.5     3244.5     3244.5      774.0     3766.5     5343.0     2765.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     4638.0     2200.5     3244.5     3244.5     1751.0     3935.0     4767.5 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     2765.0     5927.0     5011.5     2200.5     5011.5     6558.5     6558.5 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     1596.0     3766.5     4791.5     3051.0     5501.5     1596.0     3766.5 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     5927.0     4226.0     6223.5     4226.0     6618.5     5343.0     2989.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     2487.5     4452.0     5792.5     2765.0     5343.0     5770.0     5770.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     5770.0     4852.0     4852.0     4852.0     4852.0     6223.5     6223.5 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     5501.5     5501.5     6940.5     6940.5     6940.5     5770.0     5770.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     6223.5     6223.5     4419.5     2913.0     3482.5     5488.0     1114.5 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     3051.0     4638.0     1297.0     2612.0     4452.0     4452.0      928.5 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     5343.0     1596.0     3517.0     4822.5      928.5     1596.0     5011.5 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     3517.0     5458.0     1446.5     2612.0     4822.5     3935.0     6904.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     3962.5     5927.0     4638.0     3517.0     4510.5     4510.5     1596.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     3051.0     3244.5     3244.5     4822.5     6357.5     3517.0     5190.5 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     2552.0     5458.0     1343.0     4452.0     2200.5     3766.5     2200.5 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     4419.5     1446.5     3517.0     2989.0     5190.5     4791.5     6904.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     5927.0     4822.5     3962.5     2200.5     1596.0     3998.5     5634.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     2940.0     1402.0     5343.0     1956.5     1446.5     2612.0     3051.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     3766.5     2765.0     4638.0     5343.0     2765.0     5927.0     1114.5 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     5343.0     6972.5     6972.5     6543.0     2612.0     7305.0     2200.5 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     2200.5     5011.5     5011.5     5011.5     3998.5     4822.5     4822.5 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     1596.0     3766.5     5927.0     2200.5     3244.5     5011.5      774.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     2765.0     5011.5     1866.5     5488.0     1796.0     2940.0     4791.5 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     1796.0     6051.0     4638.0     4638.0     4638.0     4638.0     1114.5 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     3244.5     1596.0     1596.0     4638.0     5343.0     4638.0     1596.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     1596.0     2200.5     2200.5     4226.0     6223.5      928.5     3998.5 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     1114.5     4226.0     1114.5     3244.5     1866.5     3998.5     4226.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     6840.0     1343.0     2487.5     4452.0     2437.5     5634.0     6223.5 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##      192.0     4638.0     2765.0     5343.0     2765.0     5927.0     5927.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     3244.5     3244.5     3766.5     3244.5     3766.5     2765.0     3998.5 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     4822.5     4226.0     1596.0     2765.0     4638.0     4226.0     4226.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     4226.0     4226.0     1402.0     1402.0     7124.5     7124.5     6558.5 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     6525.0     4419.5     5213.5     6051.0     3051.0     7306.0     6051.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     6051.0     6051.0     6051.0     5011.5     5634.0     3962.5     4487.5 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     3482.5     2552.0     5488.0     1751.0     7104.0     2200.5     4226.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     6223.5     2200.5     3766.5     5011.5     2200.5     3766.5     4226.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     4226.0     6840.0      928.5     3998.5     6076.0      192.0     2200.5 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     3244.5     1114.5     4226.0     1114.5     3244.5     1343.0     4452.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     2200.5     5634.0     3051.0     4044.0      416.5     5011.5     6618.5 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     1114.5     4226.0     6618.5     2487.5     4452.0     4226.0     1297.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     3482.5     1114.5     4638.0     4638.0     2200.5     6223.5     2200.5 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     5011.5     1114.5     3244.5     1343.0     2940.0     2940.0     1596.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     3766.5     5927.0     5809.5     4226.0     3244.5     3517.0     5011.5 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     2989.0     1596.0     3766.5     3766.5     1866.5      416.5     4226.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     6223.5     4638.0     4638.0     3244.5     4226.0     2989.0     2989.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     4452.0     6895.5     5634.0     5634.0     5634.0     5634.0     4226.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##      275.0     3998.5     5488.0     5792.5     5792.5     5792.5     5792.5 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     7147.0     7314.5     3244.5     6223.5     6223.5     6448.5     5011.5 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     7301.0     6223.5     6223.5     6223.5     6223.5     6223.5     1956.5 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     3517.0     5190.5     6223.5     2487.5     3595.0     5233.5     7031.5 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     6389.5     1866.5     3556.0     5213.5     6379.0      416.5       85.5 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     1596.0      774.0      192.0     5634.0     7212.0     1596.0     3244.5 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     5011.5     5011.5     2913.0     2913.0     5170.0     4419.5     3244.5 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     5634.0     3244.5     5927.0     2765.0     5011.5     5634.0     5343.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     4638.0     4638.0     6223.5     6223.5     1796.0     3517.0     5792.5 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     2940.0     4452.0     6369.0     1596.0     4226.0     1297.0     1297.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     4767.5     3962.5     1596.0     4226.0     1596.0     5011.5     2200.5 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     3766.5     5011.5     4226.0     5927.0     2765.0     3766.5     2765.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     2765.0     6223.5     6223.5     1596.0      774.0     3766.5     4226.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     3244.5     2487.5     5190.5     5011.5     6448.5     4226.0     6618.5 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     2200.5     3766.5     5927.0       85.5      774.0     2200.5     2200.5 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     5011.5     6223.5     7031.5     4638.0     5634.0     6448.5     1114.5 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     4226.0     5011.5     5011.5     5927.0     5927.0     6840.0     6840.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     5343.0     5343.0     6223.5     6223.5     3766.5     3766.5     4638.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     4638.0     6223.5      774.0     3244.5     5634.0     5927.0     5343.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     6618.5     1596.0     4226.0     5011.5     5011.5     6448.5     6448.5 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     3766.5     5927.0      774.0     2200.5     3766.5     3766.5     1596.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     1866.5     4487.5     6448.5     7031.5      416.5     2200.5     1114.5 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     5927.0     6448.5     2200.5     5011.5     5011.5     6840.0     6448.5 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     2765.0     3766.5     5634.0     5634.0     5343.0     7031.5        5.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     2765.0     5927.0     6940.5     1114.5     3766.5     3962.5     5792.5 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     1596.0     4638.0     1596.0     3766.5     6618.5     2200.5     5213.5 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     5927.0     6940.5     7242.5     6223.5       15.5     6940.5     7242.5 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     5343.0     1866.5     2765.0     4638.0     3766.5     3766.5     7031.5 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     3244.5     1114.5     3244.5     5011.5     6738.5     6448.5     7104.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     6618.5     5343.0     5343.0     5634.0     6940.5     6223.5     5343.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     3595.0     5233.5     7088.0     6618.5     7296.5     7212.0     6223.5 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     1956.5     3517.0     5190.5     2487.5     3595.0     5233.5     3766.5 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     5011.5     6448.5     1751.0     4767.5      416.5     2200.5       85.5 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##      774.0     3244.5     5634.0     5634.0      928.5     2989.0     1114.5 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##      192.0      416.5       85.5     1114.5     2200.5     4638.0     1114.5 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     2765.0     4638.0     5927.0     5011.5       85.5     2200.5     6223.5 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     4638.0      774.0     1596.0     2765.0     4226.0      416.5     4638.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     3766.5     1596.0     5343.0     3766.5     3766.5     5343.0     4638.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     3766.5     3244.5     4452.0      642.0     5011.5     3766.5     5634.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     4226.0     4638.0     5343.0     6940.5     5011.5     6840.0     5927.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     7031.5     7212.0       15.5     6940.5     7295.0     5927.0     5011.5 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     7031.5     1114.5     2200.5     2200.5     6223.5     2765.0     2765.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     3766.5     6448.5     2765.0     1114.5     5011.5     3244.5     5634.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     5634.0     5927.0     3244.5     6840.0     5011.5     5927.0       85.5 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##      774.0     5927.0      416.5     3556.0     4638.0     5011.5       15.5 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     5343.0     6738.5     4638.0     3595.0     5233.5     7088.0     7262.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     5634.0     7031.5     6223.5     1596.0     3766.5     5343.0     6618.5 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     3244.5     5343.0     6840.0     7254.0     4226.0     5343.0     6618.5 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     7212.0     2765.0     5634.0      416.5      416.5     4226.0     4226.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     1596.0      774.0     4226.0      774.0     2200.5      416.5     2200.5 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     4226.0     1114.5     4226.0     6223.5     2765.0     5343.0      416.5 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     2200.5      192.0      774.0     3766.5     5343.0      416.5     3244.5 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     1114.5     7031.5     3244.5     5011.5     7254.0     1596.0     5634.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     5634.0     2437.5     4419.5     5770.0     3244.5     6840.0       85.5 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     1596.0     1596.0     3766.5     4638.0     5343.0     5343.0     6448.5 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     4226.0     5634.0     6618.5     7147.0     1114.5     3244.5     6223.5 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     1596.0     4638.0     4638.0     2200.5     1114.5     4487.5     1796.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     3244.5     1402.0     4487.5     5634.0     6840.0     7147.0     4226.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     5634.0     3244.5     6223.5     6840.0     5343.0      192.0     1596.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     3244.5     6223.5     4226.0     6223.5     5343.0     6738.5     1114.5 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     4226.0     6618.5     6738.5     6223.5     2765.0     5927.0     1866.5 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     4822.5     5011.5     6840.0     1114.5     4226.0     1297.0     5170.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     5927.0     6940.5       35.0     3244.5     4226.0     4226.0     4226.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     4226.0     5634.0     4638.0     6738.5     7266.5     1114.5     4226.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     3998.5     2200.5     7031.5     7212.0      141.5     2200.5     5011.5 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     6840.0     1114.5     3244.5     5011.5     3595.0     6389.5     5233.5 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     5634.0      416.5     4638.0     5927.0     7104.0     7178.5     2765.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     4638.0     7104.0     7178.5     6223.5     2200.5     4638.0     5011.5 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     6223.5     5634.0     7031.5     6223.5     7212.0     7212.0     7287.5 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     7212.0     7287.5     6223.5     7212.0     7287.5     4226.0     5927.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     3998.5     5501.5     6558.5     2989.0     4822.5     6389.5     2437.5 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     3935.0     5458.0     2765.0     4638.0     6840.0     1596.0     5343.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##      192.0     5343.0     3766.5     5343.0     6840.0      416.5     4226.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     6940.5     7266.5     1343.0     2487.5      305.5     3051.0     4852.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     4044.0     5818.0      416.5     5011.5     1596.0     3766.5     6448.5 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##      774.0     3244.5     1866.5     5213.5     6223.5     2200.5     5011.5 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     3935.0     4767.5     7120.0     3244.5     6940.5     2765.0     5190.5 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##      416.5     3556.0     5809.5      151.5     5501.5     5011.5      192.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##        5.0     3766.5     5792.5     4226.0     5634.0     7031.5     7277.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     5343.0     4226.0     6618.5     7178.5     4452.0     6681.5     2200.5 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     5634.0     3482.5     6509.0     6223.5     1956.5     4044.0       85.5 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     3517.0     5792.5     7083.0     6840.0     4226.0     5343.0     7147.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     6223.5     6904.0     5927.0     2200.5     4767.5     6618.5     3244.5 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     5927.0     7031.5     5011.5     6940.5     7266.5     2765.0     6223.5 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     5011.5     2200.5     3766.5     3766.5     3766.5     3766.5     3766.5 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     4638.0     6448.5     6940.5     6840.0     6840.0     5634.0     7254.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     6223.5     6840.0     4226.0     5011.5     6223.5     5634.0     6223.5 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     6379.0     6840.0     6223.5     1596.0     3244.5     4638.0     3556.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     5809.5     6904.0     2765.0     4638.0     6223.5     2765.0     5343.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     3244.5     5011.5     6618.5     2200.5     5488.0     2552.0     6076.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     2552.0     6076.0     2552.0     6076.0      192.0     2765.0     2765.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     4791.5     6738.5     3244.5     6223.5     4638.0     6448.5     7104.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     1596.0     2940.0     5343.0     7104.0     3244.5     3244.5     5011.5 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     6448.5     7147.0     2200.5     3766.5     5927.0     6223.5     6940.5 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     2552.0     3556.0     6379.0     1596.0     2765.0     6223.5      252.5 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     3962.5      774.0     3244.5     5792.5     5792.5     4452.0     5190.5 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     6788.0     5634.0     5634.0     7147.0      416.5     7147.0     7254.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     5458.0     3766.5     6448.5     4638.0     5634.0     2989.0      416.5 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     2765.0     2200.5     4822.5     6543.0     6681.5     7147.0     2200.5 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     5927.0     3766.5     6448.5     2200.5     5927.0      416.5     1596.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     2765.0      416.5     1114.5     3766.5     1446.5     5476.0     2200.5 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     3766.5     5927.0     3244.5     6223.5     4638.0     4638.0     7104.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     2765.0     5343.0     1114.5     3244.5     4638.0     1796.0     4791.5 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     2200.5     5818.0     2765.0     5011.5     6223.5     2765.0     5343.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     6738.5     2765.0     4638.0      416.5     1114.5     2765.0     5476.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     6788.0     7190.0     4226.0     7031.5     2200.5     1114.5     4226.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     3935.0     5170.0     5343.0     6940.5     6840.0     4510.5     6910.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     5343.0     6940.5     6618.5     5213.5     6904.0     2765.0     3766.5 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     6051.0     6558.5     3766.5     5343.0     6448.5     5343.0     3244.5 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     5634.0     6990.5     5634.0     6738.5     6223.5     6738.5     6448.5 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     6840.0     7212.0     6940.5     7178.5     7031.5     7277.0     6840.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     4452.0     4452.0     4791.5     6985.0     6840.0     3482.5     4822.5 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     6525.0     1596.0     3051.0     4044.0     2913.0     3556.0     5927.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     7178.5      870.0     2913.0     2200.5     4487.5     5170.0      774.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##      131.5     2487.5     2200.5     3998.5      928.5     1596.0     3766.5 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     3766.5     6223.5     5190.5     6910.0     5011.5     1866.5     1866.5 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     1866.5     5011.5     6618.5     2200.5     7147.0     2487.5     4452.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     1866.5     5011.5     2612.0     5792.5     2487.5     6448.5     3244.5 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     4638.0     2552.0     4487.5      928.5     2765.0     5501.5     6887.5 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     3051.0     4791.5     3766.5     5792.5     2989.0     2200.5     2200.5 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     4638.0     6223.5     5011.5     4226.0     6618.5     3935.0     3935.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     3935.0     3935.0     5634.0     5343.0     5927.0     6223.5     4044.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     6788.0      416.5     4226.0     7075.0     7075.0     6223.5     6223.5 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     6223.5     6223.5     7254.0     6618.5     7254.0     6618.5     2765.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     4638.0     6525.0     3766.5     5343.0     6738.5     2612.0     4044.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     4510.5     4638.0     5343.0     6840.0     4226.0     6618.5     3517.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     5190.5     4226.0     5190.5     7083.0     4226.0     4226.0     4226.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     4226.0     1596.0     2765.0     3766.5     3766.5     6738.5     6738.5 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     6788.0       85.5     7147.0     2552.0     3556.0     6223.5     6940.5 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     1114.5     4226.0     2765.0     6448.5      774.0     1596.0     1446.5 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     3595.0     4638.0     6940.5      416.5     1114.5     6840.0     2487.5 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     3244.5     6840.0      774.0     6940.5     2765.0     4638.0     3244.5 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     5927.0     5927.0     3244.5     6223.5     6738.5     4822.5     6076.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     5634.0     6448.5     3244.5     7075.0     2765.0     4638.0     6618.5 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     6738.5     5343.0     7031.5     4638.0      305.5      305.5     7124.5 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     6690.5     2552.0     7131.5     7124.5     5501.5     6357.5     7293.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     6357.5     6525.0     7244.5     6690.5     7271.5     5927.0     6940.5 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     4487.5     4487.5     7075.0     6979.0     6681.5     3766.5     7165.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     3766.5     5343.0     6940.5     4419.5     5458.0     6781.0     1114.5 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     3244.5     5343.0     2765.0     5927.0     1796.0     3962.5     5476.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##       85.5      774.0     1596.0      275.0     2989.0     1114.5     2200.5 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     1114.5     5011.5     3595.0     6738.5     1596.0     5634.0     1114.5 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     3244.5     5011.5     1114.5     6618.5     5343.0     2940.0     6525.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     5343.0     3556.0     6904.0      928.5     6076.0     6076.0     3766.5 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     6738.5     3998.5     6792.5     1596.0     3766.5      416.5     1114.5 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     5634.0     1596.0     3244.5     6840.0     5634.0     6618.5     5927.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     7104.0     5343.0     5927.0     1114.5     3244.5     6223.5     6357.5 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##      192.0     3766.5     6940.5     5011.5     5011.5     3766.5     2765.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     5927.0     5927.0     5011.5     7031.5     6223.5     6223.5     5634.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     5634.0     5927.0     3244.5     5011.5     6840.0     7277.0     7031.5 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     7031.5     7031.5     5343.0     6738.5     5634.0     7031.5     7031.5 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     6840.0     6223.5     6223.5     7178.5     3766.5     5011.5     7266.5 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     5343.0     1956.5     3556.0     5213.5     6904.0     1402.0     2989.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     4822.5     6673.5     4822.5     3766.5     5011.5     6223.5     6840.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##      774.0     2765.0     2200.5      416.5     2200.5     1114.5     1114.5 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     1114.5     2765.0     4638.0     5011.5      774.0      774.0     3244.5 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     4226.0      416.5     3766.5     1446.5     4044.0     1114.5     2200.5 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     2765.0     2200.5     5343.0     5634.0     6618.5     6840.0     2487.5 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     3962.5     5476.0     1114.5     2940.0     4791.5     1866.5     4487.5 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     1114.5     1114.5     4638.0     4638.0       85.5     1596.0      774.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     4226.0     2765.0     4226.0     5343.0     2765.0     5011.5     2200.5 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     3244.5      192.0      416.5     2200.5     3244.5      774.0      774.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     2765.0     3244.5     3244.5     1866.5     4822.5      774.0     3244.5 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     1596.0     5011.5     1114.5     3766.5     5927.0       35.0      416.5 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     1596.0     3766.5     2200.5     4226.0     5011.5      774.0     2200.5 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     3766.5      774.0     3766.5     6223.5     6223.5     6738.5     6738.5 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     7031.5     7031.5     2765.0     2765.0     4226.0     4226.0     3766.5 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     3766.5     6618.5     7287.5     5927.0     4226.0     5927.0     2765.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     2765.0     1596.0     4226.0     2765.0     5011.5     2200.5     2200.5 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     4638.0     4638.0     6223.5     7287.5      416.5     1596.0     3244.5 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     5011.5     7254.0     1596.0     4226.0     5634.0     6738.5     2200.5 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     4226.0     5011.5     5343.0     5343.0     5927.0     6618.5     6618.5 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     2552.0     4852.0     4638.0     5927.0     6840.0     6840.0     5927.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     6840.0        5.0     6738.5     5011.5     6223.5     4226.0     5927.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     3244.5     3766.5     2765.0     5343.0     3766.5     3766.5     5011.5 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##      774.0     2940.0     2940.0     6223.5     6840.0     5927.0     2765.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     4852.0     6558.5     5011.5     1866.5     2200.5     4226.0     3244.5 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     3244.5     3244.5     4226.0     6223.5     6940.5     5634.0     6448.5 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     3766.5     5343.0     3766.5     6223.5     5343.0     5343.0     5634.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     5634.0     5634.0     1596.0     3766.5     5927.0     5634.0     6223.5 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     4226.0     6223.5     3766.5     5011.5     6223.5     1402.0     2989.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     4822.5     3595.0     5011.5     6223.5      678.5     3595.0      416.5 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     2200.5      416.5     2200.5     1114.5     4226.0     5011.5      928.5 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     2989.0       35.0      275.0       35.0     3766.5     5343.0     1596.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     4226.0     1114.5     2765.0     5343.0     6448.5     5927.0     2200.5 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     5011.5     6738.5     4638.0     1114.5     2200.5     4226.0     2765.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##      416.5     2765.0     1596.0     1596.0     5343.0     3244.5     6618.5 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     1343.0     5011.5     5011.5     3244.5     1866.5     2989.0     3766.5 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     5634.0     7147.0     4226.0     6448.5     7301.0     7308.5     2200.5 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     4638.0     5343.0     6223.5     7147.0     7308.5     4638.0     6738.5 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     2765.0     2200.5     4638.0     1114.5     6840.0     1114.5     5011.5 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     5927.0     6223.5      774.0     4638.0     1596.0     4226.0     7147.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##      416.5     2200.5     5634.0     5927.0     3244.5     6840.0     5634.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     3517.0      774.0      774.0     5927.0       35.0      898.5      898.5 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     6448.5       15.5     4638.0     5343.0     6738.5     2200.5     4226.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     6618.5     7212.0     7031.5     5634.0     6223.5      774.0     1596.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     3766.5     4638.0     1114.5     3244.5     4226.0     5011.5     2200.5 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     4226.0     5634.0     6618.5      774.0     4226.0     1297.0     1297.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     3482.5     3482.5      774.0     2765.0     2200.5     2765.0     4226.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##      416.5     2200.5     4226.0     2200.5     1114.5     3244.5      416.5 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     3244.5      416.5     6223.5      192.0      774.0     3766.5     3766.5 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##      603.5     3517.0     3244.5     5011.5     3244.5     5011.5     6223.5 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     5634.0     1596.0     1596.0      141.5     1402.0     3556.0      416.5 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     3244.5     1114.5     7104.0     1596.0     3766.5     4638.0     6940.5 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     5343.0     6940.5     6223.5     7147.0     7287.5     7308.5     2765.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     4638.0     5343.0     1114.5     3244.5     5011.5     1114.5     2200.5 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     4638.0     4638.0     1596.0     2552.0     5213.5       85.5     1114.5 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     4226.0      774.0     1596.0     1114.5     4226.0     6223.5     7147.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##      774.0     3766.5     3244.5     1596.0     2200.5     5011.5     1114.5 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     3244.5     3244.5     5634.0     6618.5     3244.5     3244.5     3766.5 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     5343.0     1402.0     4487.5     3766.5     5343.0     2765.0     6448.5 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     4791.5     6051.0     2200.5     6223.5       85.5     4852.0     4226.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     4226.0     4226.0     4226.0     4226.0      774.0     3766.5     4638.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     5634.0     7031.5     5213.5     5011.5     3244.5     3244.5     1866.5 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     2200.5     5011.5     5011.5     2200.5     4226.0     5634.0     5343.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     6448.5     6448.5     7031.5     2765.0     3244.5     6223.5     5011.5 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     7031.5     5011.5     6223.5     7031.5     7212.0     7301.0     6223.5 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     7212.0     5634.0     6840.0     5011.5     6223.5     5634.0     5634.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     7147.0     7254.0     5634.0     6223.5     6223.5     2200.5       85.5 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     7254.0     5634.0     3595.0     5343.0     6448.5     1446.5     2487.5 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     4419.5     2552.0     3998.5     5488.0      416.5     2200.5     4226.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     1114.5     3244.5     1297.0     2437.5      678.5     1956.5     4852.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##      774.0     3766.5     1114.5     3244.5     6792.5     7273.0     1596.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     5343.0     6448.5     5927.0     2200.5     1343.0     6369.0     4226.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##      774.0     2765.0     1596.0     4226.0     1866.5     3998.5     3935.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##      678.5     1446.5     1446.5     5233.5     5818.0     3244.5     2487.5 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     1343.0     3595.0     2200.5     1796.0     3962.5     5458.0     6781.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     5927.0     1596.0        5.0     1596.0     6543.0     1596.0     3766.5 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     5927.0     6738.5     1114.5     1114.5     4226.0     5488.0     4822.5 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     6792.5     5634.0     6618.5     1114.5     3766.5     4452.0     2913.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     4767.5     2200.5     2200.5     4638.0     6448.5     3766.5     6738.5 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     5927.0     7031.5     6940.5     3962.5     3962.5     5927.0     5458.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     7031.5     6618.5     6940.5     5011.5     4226.0     3766.5     5927.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     7178.5     7104.0     4226.0     4791.5     6525.0     6525.0     6525.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     6525.0     6525.0     7031.5     6448.5     4226.0     6840.0     6223.5 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     6509.0     6618.5     6840.0     6223.5     6840.0     5011.5     6223.5 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     5634.0     6223.5     4638.0     6840.0     6223.5     2765.0     4638.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     5927.0     1596.0     2765.0     3766.5     1596.0     3244.5     5343.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##      958.0     4044.0     1596.0     3766.5     5634.0      898.5     2200.5 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     1866.5     3556.0     1866.5     3556.0     1866.5     3556.0     1596.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     5343.0     2200.5     3517.0     5011.5      603.5     3556.0     5343.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     6448.5     7031.5     1402.0     2765.0     1114.5     3244.5     5458.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     6972.5     2765.0     5343.0     5927.0     1114.5     2765.0     4638.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     5343.0     4638.0     4226.0     5634.0     5011.5     3051.0     6092.5 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     5501.5     3244.5     5343.0     2200.5     4638.0     3766.5     3766.5 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##      774.0     1596.0     5011.5     1596.0     1596.0     2765.0     6448.5 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     2765.0     2765.0     3962.5     5343.0     6940.5     4226.0     6738.5 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     5011.5     5343.0     5343.0     7178.5     6379.0     7086.0     1596.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     3766.5     3517.0     5190.5     1114.5     3244.5     1114.5     3244.5 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     2200.5     4226.0     5927.0     1114.5     2200.5     4638.0     2200.5 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     3556.0     2200.5     3766.5     5343.0     2200.5     4226.0     3244.5 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     1596.0     6448.5     1596.0     4226.0     2200.5     4226.0     5011.5 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     2200.5     3244.5     5770.0     7075.0     4226.0     5927.0     6738.5 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     1114.5     4226.0     5011.5     2765.0     4638.0     2765.0     3244.5 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     5634.0     3244.5     4226.0     5634.0     1114.5     5011.5     2200.5 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     5343.0     1114.5     5233.5     6092.5     2200.5     3244.5     5634.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     2200.5     5011.5     4226.0     6223.5     5927.0     2200.5     4638.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     4638.0     6448.5     3935.0     6681.5     2200.5     4226.0     5634.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     2765.0     3766.5     6990.5     5634.0     4452.0     4226.0     5011.5 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     4226.0     4226.0     6223.5     6840.0     5634.0     5634.0     5634.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     6223.5     5634.0     5634.0     5634.0     4791.5     5634.0     5927.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     3766.5     5927.0     6895.5     1114.5     2989.0     4452.0     1114.5 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     2487.5     3962.5     3962.5      928.5     4791.5     1402.0     2765.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     4419.5     1114.5     3244.5     2200.5     2989.0     4452.0      958.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     1796.0     3998.5     1866.5     3517.0     3517.0     4822.5     3244.5 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     2612.0     4767.5     4767.5     6223.5     7212.0     7147.0     2200.5 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     3244.5     4452.0     3935.0     5458.0     2552.0     5488.0     3962.5 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     6738.5     3244.5     5634.0     3517.0     5488.0      642.0     2487.5 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     4791.5     6795.5     2200.5     4452.0     3766.5     4638.0     2940.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     7212.0      774.0     2552.0     4226.0     5770.0     1866.5     4767.5 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     4226.0     4226.0     4226.0     4226.0     6895.5     6895.5     6895.5 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     6895.5     5343.0     7294.0      774.0     5011.5     5927.0     5927.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     6887.5     4419.5     6887.5     7212.0     6618.5     3244.5     3244.5 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     6840.0     2765.0     4638.0     4638.0     1114.5     2200.5     5634.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     2612.0     4044.0     4510.5      416.5     2200.5     4638.0      416.5 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     3244.5     1596.0     5927.0     5634.0     2940.0     4791.5     4226.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     4226.0     4226.0     4226.0     1114.5     3244.5     1114.5     1114.5 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     3766.5     4638.0     3766.5       85.5     7031.5     3517.0     7235.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     4226.0     6223.5     2487.5     5792.5      774.0     5634.0     2765.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     5011.5     1114.5     3244.5     2200.5     5011.5      928.5     1866.5 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     6076.0     5634.0     5011.5     5634.0     1114.5     7147.0     5011.5 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     6618.5     7031.5     5927.0     5927.0     6223.5     3244.5     3766.5 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     7104.0     3556.0     5011.5     4226.0     5343.0     6223.5     3766.5 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     5634.0     6940.5     5343.0     4638.0     6738.5     6738.5      561.5 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     7075.0     6525.0     5501.5     1796.0     7131.5      898.5     4419.5 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     6379.0     7075.0     2552.0     6357.5     6979.0     6558.5     6051.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     6738.5     5011.5     3962.5     4487.5     7304.0     2552.0     4638.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     3766.5     7165.0     2200.5     4226.0     5634.0     1114.5     3244.5 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     4638.0     1114.5     2765.0     3766.5     2765.0     5927.0      774.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     2765.0     6448.5       85.5     5011.5     6618.5      275.0     2989.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     3244.5     5634.0     2765.0     6223.5     2200.5     5634.0     2200.5 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     3244.5     2200.5     5634.0     6840.0     5011.5     5011.5     5011.5 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     4638.0     6940.5     2765.0     1114.5     3244.5      774.0     4226.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     4226.0     1596.0     3766.5     3244.5     5634.0     1596.0     3766.5 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     1596.0     3766.5     5927.0      416.5     1596.0     3766.5     6357.5 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     6223.5     2200.5     5927.0     4638.0     7178.5     1114.5     4638.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     6223.5     5927.0      774.0     3244.5     7104.0     6618.5     4226.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     2765.0     3766.5     2989.0     2989.0     1956.5     4044.0     5634.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     5634.0     6223.5     6223.5     6738.5      416.5     2200.5     4638.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     6738.5     6738.5     7178.5     6738.5     5927.0     7312.0     3244.5 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     6223.5     6223.5     6448.5     2200.5     2200.5     7031.5     3244.5 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     5011.5     7212.0     5011.5     3766.5     5011.5     6223.5     6895.5 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     3244.5     5011.5     6558.5     7031.5     7246.0     2200.5     3766.5 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     5343.0     7104.0       85.5      192.0     5634.0       85.5      192.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     1596.0       35.0      774.0     2200.5     4226.0     7104.0      192.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##      192.0     2200.5     1114.5      561.5     3482.5     1114.5     3766.5 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     2200.5     4638.0     5634.0     5927.0       85.5       15.5     1596.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##      192.0     1114.5     2765.0     4638.0     1114.5     2765.0     5343.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     3244.5     5927.0     2200.5     2200.5     5170.0     5170.0     1596.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     5343.0      416.5     3766.5     4638.0     5343.0     6618.5     1596.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     3244.5     1114.5     5634.0      774.0      774.0     2200.5     2200.5 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     1114.5     2200.5     1114.5     3766.5     4638.0     3766.5     5927.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##      774.0     3244.5     4226.0     6223.5     1114.5     2200.5     4226.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     6448.5     6840.0     7104.0     7282.0      774.0     2200.5     3766.5 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     4226.0     5343.0     6223.5     2200.5     5011.5      416.5      416.5 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     1596.0     1596.0     2765.0     2765.0     3766.5     3766.5     5011.5 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     5011.5     1114.5     1114.5     6223.5     7031.5     6223.5     5927.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     6840.0     2989.0     2989.0      774.0     3244.5     1596.0     4226.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     1114.5     1114.5     3766.5     3766.5     1596.0     5343.0     6223.5 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     6738.5     7031.5     7277.0      416.5     1402.0     4487.5      192.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     2200.5     2765.0     5011.5     3244.5     6940.5     6940.5     1114.5 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     2200.5     2200.5      774.0      774.0     3244.5     3244.5     5343.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     5343.0      416.5     1596.0      774.0     6223.5     1114.5     3244.5 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     4226.0     5927.0      774.0     3766.5     4226.0     6223.5     6223.5 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     5634.0     2765.0     6738.5     5927.0     6223.5     2765.0     3766.5 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     1114.5       35.0     3244.5     4226.0     5343.0     1866.5     1596.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     3766.5     6618.5     6618.5     5927.0     5343.0     5634.0     6738.5 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     2765.0     3766.5     3244.5     4226.0     3766.5     5927.0     6940.5 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     6940.5     5343.0     6940.5     6223.5     2200.5     4226.0     6223.5 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     7239.0     7239.0     7298.0     6223.5     1956.5     3556.0     5213.5 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     3244.5     5011.5     6558.5     1446.5     3051.0     4852.0      774.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     1596.0      870.0     5458.0     1114.5     2765.0     1114.5     3244.5 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     4226.0     2913.0     4767.5      774.0     1596.0     1114.5     1114.5 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     5011.5     4226.0     5927.0     3766.5     5011.5     6738.5     7266.5 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     5634.0       85.5     3244.5      416.5     5634.0      774.0     1596.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     2765.0     1114.5      416.5     6448.5     4638.0      192.0     3766.5 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     7031.5     3244.5     5343.0     6223.5     6618.5     5343.0     4452.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##      642.0     5634.0      774.0     3244.5     3766.5      774.0     2765.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     2765.0     1114.5     3244.5     2765.0     2765.0     3766.5       15.5 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     1114.5     1114.5      774.0     2200.5     4638.0     4226.0     5634.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##      416.5     3244.5     3766.5     3766.5     3766.5     6448.5     3766.5 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     5011.5     1114.5     5011.5     6618.5     7147.0     5927.0     1114.5 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     3244.5     6448.5     5343.0       85.5      774.0      774.0     5343.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     3766.5     4226.0     1114.5     4638.0     6738.5     5634.0     5634.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     1596.0     3766.5     5927.0     5927.0     5634.0     6618.5     6223.5 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     3766.5     5343.0     6448.5     7178.5     4226.0     5343.0     6618.5 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     7031.5     2200.5     3244.5     5343.0     5927.0      416.5     3244.5 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##      416.5      416.5     2200.5     2200.5       85.5      192.0     6223.5 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##      192.0     2200.5     1114.5     3244.5     5634.0      416.5     3244.5 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     5634.0      416.5     4638.0     2765.0     5927.0     1596.0     3766.5 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     6940.5     7266.5     2200.5     6223.5     1114.5     3244.5      774.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     2765.0     5927.0      416.5     2200.5      416.5     2437.5     4419.5 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     5770.0       85.5     1114.5       85.5     1596.0      774.0     2765.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     3766.5     5343.0     2765.0     2765.0     1114.5     2200.5     3244.5 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     3244.5     1114.5     3244.5     5011.5     3766.5     6448.5     7104.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     5343.0     4638.0     1796.0     4487.5     6223.5     1402.0     4487.5 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     3766.5     5927.0     6618.5     4226.0     5634.0     3766.5     6448.5 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     7178.5     5343.0     4226.0     5634.0     3244.5     1596.0     2200.5 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     5011.5     5343.0     6738.5     1114.5     4226.0     6618.5     3244.5 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     6223.5     5927.0     2765.0     1751.0     4767.5     2200.5     5634.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     1114.5     4226.0     3482.5     5170.0     1114.5     4226.0       15.5 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     6223.5     4226.0     4226.0     4226.0     4226.0     3244.5     3556.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     5213.5     5809.5      416.5     3244.5     4452.0     5927.0     4638.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     6738.5      141.5     2200.5     5011.5     6840.0     2765.0     4638.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     5927.0     4510.5     5818.0     6697.0     1596.0     1114.5     1114.5 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     3244.5     6618.5     6840.0      774.0     2765.0     5927.0     6448.5 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     2200.5     6223.5     4638.0     6618.5     7147.0     7031.5     7254.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     7212.0     7287.5     6223.5     7212.0     7212.0     7287.5     6223.5 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     7212.0     7287.5     4226.0     5927.0     1596.0     2765.0     4638.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     2989.0     4822.5     6389.5     3482.5     5170.0     6673.5      416.5 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     1114.5     2200.5      774.0     2765.0      192.0     2765.0     1114.5 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     3244.5     5927.0     2200.5     6940.5      416.5     3766.5     1343.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     1796.0      305.5     4852.0     3051.0     4044.0     5818.0      416.5 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     5011.5     1596.0     3244.5     5343.0     3517.0     5476.0     2940.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     5792.5     3244.5     3244.5     5634.0     1446.5     2612.0     2612.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     6448.5     6448.5     1343.0     3595.0     3766.5     1297.0     4419.5 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##      151.5     4852.0       15.5     4226.0       35.0     5792.5     5792.5 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     1114.5     2200.5     5011.5     6840.0     1114.5      774.0     6738.5 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     7147.0      928.5     3998.5      416.5     3244.5     3482.5     5458.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     5634.0     1956.5     4044.0     3244.5     3517.0     5792.5     7083.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     1596.0     7031.5     5011.5     2765.0     6618.5     5927.0     6940.5 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     6840.0     6781.0     3244.5     7031.5     1596.0     4226.0     7104.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     3766.5     3766.5     4638.0     3766.5     5011.5     5927.0     6448.5 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     6448.5     6448.5     6448.5     6448.5     4638.0     6448.5     6940.5 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     7212.0     7212.0     7031.5     5011.5     6223.5     6840.0     4638.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     5011.5     6223.5     7104.0     6223.5     6840.0     6840.0     6223.5 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     1596.0     3244.5     4638.0     1596.0     4226.0     6223.5     3556.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     5213.5     6543.0      774.0     3244.5      192.0      774.0     1596.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     2200.5     5488.0     2552.0     6076.0     2552.0     6076.0     2552.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     6076.0     3962.5     6525.0      870.0     2200.5     3766.5     2200.5 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     5011.5      192.0     2200.5     3244.5     2765.0     4791.5      416.5 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     2200.5     3244.5     5011.5      192.0     1114.5     2765.0     2200.5 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     3766.5     5343.0      416.5      416.5     1114.5     2200.5     4226.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##      416.5     1114.5     2765.0      898.5     4791.5     4226.0     6618.5 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     5011.5     5011.5      774.0     1596.0     3766.5       35.0       35.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##      416.5     7147.0      416.5      416.5     6509.0      774.0     3766.5 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##      774.0     1596.0     6379.0     2765.0      416.5     5927.0     2552.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     5213.5     4510.5     6681.5     1114.5     3244.5     5011.5     6840.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     1114.5     3766.5     2765.0     4638.0     5927.0     6448.5     6940.5 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     6940.5     1446.5     5476.0     3766.5     5011.5     6618.5     1114.5 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     3244.5     4638.0     1114.5     3244.5     2765.0     5343.0     3766.5 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     5343.0     1114.5     2940.0     6051.0      151.5     2612.0     1596.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     4226.0     4226.0     1596.0     4638.0     6448.5     5343.0     6940.5 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     3766.5     5927.0     7178.5      416.5     1114.5     3244.5     3244.5 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     4226.0     5927.0     1114.5     6223.5     5170.0     3935.0     2765.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     5927.0     3244.5      774.0     2765.0     1114.5     3244.5     1114.5 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##      898.5     2940.0     1596.0     2765.0     6448.5     1866.5     1596.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     3766.5     6940.5     2200.5     7104.0     5634.0     4452.0     5634.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     6940.5     6618.5     6940.5     7178.5     7212.0     6840.0     6738.5 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     6448.5     7031.5     6840.0     7277.0     6990.5     6990.5     6985.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     4791.5     6840.0     1866.5     2913.0     4510.5     3517.0     5634.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     6697.0     3517.0     5213.5     6379.0     7236.0      603.5     2200.5 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     1114.5     2913.0     4510.5      252.5      252.5     5634.0     4487.5 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     6558.5     3482.5     4638.0     6681.5     3517.0     6051.0     1751.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     4044.0     3766.5     3556.0     5213.5     5213.5      774.0     1596.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     2200.5     7147.0     3556.0     6092.5     3051.0     5343.0      898.5 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     3998.5      958.0     2612.0     3244.5     6051.0     2989.0     6840.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     3482.5     6558.5     1596.0     4044.0     3962.5     6092.5     6618.5 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     4510.5     5170.0     2200.5     2765.0     5213.5     6618.5     5011.5 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     2200.5     3766.5     1446.5     1446.5     1446.5     1446.5     2989.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     2989.0     2989.0     2989.0      603.5     1446.5     1596.0     6051.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     7075.0     7075.0     5170.0     3482.5     5770.0     6223.5     7254.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     7212.0     7277.0     7031.5     2200.5     4226.0     6223.5     3244.5 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     5011.5     5011.5     3935.0     5770.0     6972.5      416.5     1114.5 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     4638.0      416.5     2765.0     2765.0     2765.0     7178.5     1343.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     3517.0     6076.0     6076.0     6076.0     6076.0     2765.0     5927.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##      416.5      416.5     2765.0     2765.0     2765.0      192.0     7147.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     1402.0     2552.0     1596.0     2765.0     1114.5     2200.5     2200.5 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     5011.5     3766.5     5634.0     4419.5     6673.5     1596.0     2765.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##      416.5     1114.5     2200.5     5190.5     5634.0     1114.5       85.5 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     3244.5     4226.0     6223.5      416.5     5927.0     5927.0     3244.5 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     6223.5     4638.0     5770.0     1114.5     2200.5     4226.0     7104.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     5458.0     1596.0     3766.5     6223.5     5927.0     7031.5     5343.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     5927.0      305.5      305.5     6690.5     7124.5     6979.0     2552.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     7124.5     7281.0     6525.0     3051.0     6051.0     6979.0     6690.5 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     7244.5     7271.5     7031.5     5634.0     4487.5     6979.0     2552.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     7233.5     6369.0     2765.0     5011.5     1114.5     2765.0     3244.5 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     1114.5     2765.0     3766.5     4226.0     5927.0     6940.5      774.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     2765.0      642.0     1402.0     2552.0      192.0     2200.5     5011.5 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     1796.0     5476.0     1114.5     5011.5     1114.5     3244.5      928.5 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     1866.5     1114.5     5634.0     1114.5     3244.5     5011.5     1114.5 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     5927.0     6223.5      774.0     3766.5     5343.0     1114.5     3244.5 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     2612.0     6697.0     6697.0     2200.5     5634.0     2200.5     4226.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     4510.5     7088.0     2913.0     5458.0     7120.0     2765.0     4638.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     5927.0     3244.5     2200.5     7178.5     3998.5     6618.5     2200.5 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     2200.5     6618.5     4226.0     5770.0     1596.0     6940.5       85.5 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     3766.5     3766.5     6448.5     5927.0     4226.0     4226.0     1114.5 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     5011.5     5011.5     5634.0     4226.0     5011.5     6840.0     1114.5 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     3244.5     6223.5     5011.5     5011.5     5011.5     5011.5     7178.5 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     6940.5     2552.0     3556.0     3556.0     4226.0     6223.5     6223.5 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     3766.5     7178.5     5011.5     5343.0     7266.5     3766.5     5011.5 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     6223.5     6840.0     2200.5     3766.5     5343.0     5343.0     6738.5 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     3595.0     5011.5     6448.5     6738.5       85.5      192.0     5011.5 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##       85.5      192.0     5011.5     5011.5      774.0     2200.5     4226.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     5011.5      774.0      774.0     4226.0     5011.5      561.5     3482.5 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     2612.0     4852.0     2200.5     3244.5     5011.5     5343.0      416.5 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##      774.0     2765.0     3766.5     1114.5     2765.0     5343.0     1114.5 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     2765.0     4638.0     3766.5     5634.0     3766.5     3766.5     6223.5 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     6223.5      192.0     2200.5     1114.5     4638.0     3766.5     4638.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     5634.0     2765.0     5011.5     2765.0     4638.0     1596.0     1596.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     4226.0     4226.0     1114.5     1114.5     1114.5     3766.5     3766.5 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     2200.5     5011.5     1596.0     4226.0     2200.5     5011.5     1114.5 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     2200.5     5634.0     2765.0     4226.0     5927.0     6448.5      774.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     3244.5     5343.0      416.5     1596.0     4226.0     6223.5     6840.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##      416.5      416.5     1596.0     1596.0     3766.5     3766.5     3766.5 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     3766.5     5011.5     5011.5     1114.5     1114.5     5011.5     5011.5 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     4226.0     2765.0     4226.0     3517.0     3517.0      774.0     3244.5 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     4226.0     5927.0     2200.5     2200.5     6223.5     6223.5      192.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     1596.0      774.0     2765.0     4638.0     5927.0     1596.0     5634.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     6738.5      192.0     2200.5     2765.0     5011.5     1596.0     4638.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     4638.0     2200.5     5011.5     5011.5     6618.5     6618.5     4638.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     1596.0     4226.0     4226.0      416.5     1596.0     6448.5     1114.5 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     5011.5     6223.5      192.0     1596.0     2200.5     5011.5     2765.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     5343.0     6840.0     5634.0     5011.5     5927.0     5343.0     5343.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     4226.0     5634.0     4226.0       85.5     6223.5     7031.5     5927.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     1866.5     3766.5     5343.0     5927.0     5927.0     6618.5     4638.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     2765.0     4226.0     1596.0     4226.0     4638.0     5927.0     6618.5 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     6940.5     6223.5     5634.0     5634.0     5343.0     6940.5     2200.5 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     4226.0     6223.5     5634.0     6223.5     6840.0     6223.5     3766.5 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     5011.5     6223.5     2200.5     3766.5     5343.0     1866.5     3556.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     5213.5     1596.0     2765.0     2913.0     6035.0     5011.5     6618.5 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     1114.5     4226.0     5011.5     2913.0     4767.5      774.0     2765.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##       85.5     3766.5     6448.5     1596.0     4226.0     1596.0     3244.5 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     5634.0     6618.5     6223.5     2200.5     5011.5     3766.5     6223.5 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     2765.0     3766.5     5927.0     2765.0      416.5     5343.0     1596.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##      192.0     3766.5     5927.0     7031.5     1343.0     6618.5     6618.5 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     5343.0     1866.5     2989.0     3766.5      774.0     3244.5     3766.5 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     3766.5     3766.5     5927.0     1114.5     3244.5     2765.0     5011.5 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     6223.5     7308.5     5927.0     6448.5     4638.0     2200.5     4638.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     1114.5     5011.5      416.5     3244.5     5927.0     6223.5      774.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     4638.0     1596.0     7147.0     4226.0     2200.5     5011.5     5634.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     5927.0     1114.5     3244.5     5927.0     3766.5     1596.0      774.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     6618.5     3766.5     2765.0     2765.0     4226.0     7104.0     6223.5 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     4638.0     6738.5     2200.5     4226.0     6223.5     6840.0     7239.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     7239.0     6223.5     2765.0     3766.5     5343.0     5343.0     2200.5 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     3244.5     4226.0     5011.5     2200.5     4226.0     5634.0     6618.5 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##      416.5     3244.5     1297.0     1297.0     3482.5     3482.5      416.5 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##      192.0     2200.5      192.0     5634.0     2200.5     4226.0     6223.5 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##      416.5     1114.5     3244.5      416.5     2200.5      416.5     4226.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##      774.0     2765.0     6738.5     7104.0     2487.5     5190.5     3244.5 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     3244.5      774.0     2765.0     3766.5      416.5     2200.5      416.5 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##      141.5     1402.0     4487.5      416.5     3244.5     1114.5     5634.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##      774.0     2765.0     3766.5     6738.5     2765.0     5927.0     1114.5 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     2200.5     3244.5     5011.5     2765.0     4638.0     5343.0     1114.5 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     3244.5     5011.5     4638.0     5343.0     5343.0     5343.0     5011.5 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     2552.0     5213.5       85.5     1114.5     3766.5      774.0     1596.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     1596.0     4638.0     6448.5     4226.0     5011.5     6618.5     3244.5 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     1596.0     2200.5     5011.5     1114.5     3244.5     4638.0     6448.5 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     6448.5     3244.5     3244.5     5343.0     3766.5     1297.0     4419.5 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##      774.0     2765.0     4226.0     6840.0     5476.0     6525.0     2200.5 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     5011.5     2765.0     6940.5     4226.0     4226.0     4226.0     4226.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     6840.0      642.0     3556.0     5213.5     5634.0     7031.5     5476.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     5634.0     3244.5     3244.5     1866.5     2200.5     5011.5     5011.5 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     2200.5     4226.0     5634.0     2765.0     4638.0     4638.0     3766.5 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     3766.5      416.5     2200.5     5011.5     5634.0     3244.5     5011.5 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     5634.0     6618.5     6223.5     7301.0     7212.0     7031.5     7212.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     6618.5     6840.0     7147.0     7254.0     5634.0     5634.0     5634.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     6223.5     6223.5     2200.5       85.5     7254.0     5634.0     2765.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     3766.5     5343.0     1446.5     2487.5     4419.5     2913.0     4767.5 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     6357.5      416.5     1114.5     3244.5     1114.5     3244.5     1297.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     2437.5      678.5     1956.5     4852.0     4226.0     6448.5      416.5 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     2200.5     5488.0     7129.0     1596.0     6448.5     5343.0     5927.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     2200.5     1343.0     6369.0     5634.0      416.5     2200.5     2552.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     4822.5     3517.0     5190.5     3935.0     1446.5     2612.0     2913.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     6035.0     6035.0     6448.5     2487.5      898.5     3051.0     2200.5 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     1297.0     3482.5     5458.0     6781.0     1596.0     5634.0     3766.5 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     1343.0     6543.0     1596.0     2765.0     5343.0     5927.0     5343.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     1114.5     4226.0     4226.0     1343.0     4452.0     2765.0     4638.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     1114.5     3766.5     4452.0     3482.5     5458.0     6840.0     3244.5 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     5343.0     6448.5     5343.0     5634.0     5927.0     2765.0     2200.5 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     5343.0     5927.0     6738.5     2913.0     3766.5     5927.0     4638.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     3244.5     4638.0     6738.5     6738.5     7212.0     7104.0     4226.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     4791.5     6525.0     6525.0     6525.0     6525.0     6525.0     7031.5 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     6448.5     4226.0     6223.5     6840.0     6509.0     6618.5     6840.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     6223.5     7031.5     5011.5     6223.5     5634.0     6223.5     4638.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     6840.0     6223.5     2765.0     4638.0     5927.0     1596.0     2765.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     3766.5     1596.0     3244.5     4226.0      958.0     4044.0     1596.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     3766.5     5634.0      898.5     2200.5     1866.5     3556.0     1866.5 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     3556.0     1866.5     3556.0     2765.0     5927.0      416.5     1343.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     2765.0      603.5     3556.0      192.0     1114.5     5011.5     1402.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     2765.0     1596.0     3766.5     5458.0     6972.5      774.0     2200.5 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     3766.5     1114.5     2765.0     4638.0     5343.0     4638.0     3244.5 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     5011.5     5011.5      958.0     4852.0     4044.0     2200.5     4638.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     2200.5     4638.0     2765.0     2765.0      774.0     1596.0     4226.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     1596.0     1596.0     2765.0     6448.5     2765.0     2765.0     3962.5 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##      774.0     4638.0     4226.0     6738.5     5011.5     3766.5     3766.5 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     6448.5     1596.0     3766.5     1596.0     3766.5     3517.0     5190.5 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     1114.5     3244.5     1114.5     3244.5     3766.5     5343.0     6618.5 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     2200.5     3244.5     5343.0     2200.5     3556.0     3244.5     4638.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     5927.0     2200.5     4226.0     3244.5     1596.0     6448.5     2765.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     5011.5     5343.0     6448.5     1596.0     2200.5     3244.5     5458.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     6781.0     3244.5     5343.0     5343.0     1114.5     4226.0     5011.5 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     2765.0     5927.0     2765.0     3244.5     5634.0     1114.5     2200.5 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     4226.0     1114.5     5011.5     7104.0     3766.5     3244.5     6092.5 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     5233.5     2200.5     3244.5     5634.0     2200.5     5011.5     1114.5 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     4226.0     5927.0     2200.5     4638.0     3766.5     5927.0     3935.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     1866.5     2200.5     4226.0     5634.0     2765.0     6448.5     4452.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     5634.0     6990.5     5011.5     5634.0     5011.5     5011.5     6840.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     6223.5     5011.5     5011.5     5634.0     5634.0     6223.5     5634.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     5634.0     6985.0     4791.5     5927.0     1596.0     2989.0     4638.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     3244.5     4226.0     6223.5     1446.5     2913.0     4419.5     4419.5 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##      870.0     4791.5      928.5     2487.5     4044.0      192.0      416.5 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     2200.5     2612.0     3935.0     4852.0     6076.0     7239.0     2940.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     5170.0     1866.5     4419.5     5634.0     3244.5     5170.0     5170.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##      898.5     1956.5     7147.0     2200.5     3766.5     5501.5     2940.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     4638.0     1751.0     4638.0     2913.0     5458.0     3244.5     6904.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     3556.0     5501.5     1402.0     5818.0     1596.0     3051.0     3556.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     5792.5     7031.5     3556.0     3962.5     7212.0     4638.0     6690.5 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     7212.0     5476.0     3244.5     5190.5     2765.0     2765.0     2765.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     2765.0     4638.0     4638.0     4638.0     4638.0      774.0     1751.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     1114.5     5011.5     6076.0     6076.0     5927.0     5343.0     5927.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     7212.0     6618.5     7031.5     7031.5     5634.0     2200.5     4226.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     6223.5     1114.5     2200.5     5634.0     2913.0     5170.0     6781.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##      416.5     2200.5     4638.0      416.5     3244.5     1596.0     4638.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     4638.0     1796.0     3962.5     4226.0     4226.0     4226.0     4226.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     1114.5     4226.0     1114.5     1114.5     3766.5     4638.0     3766.5 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##      192.0     7104.0     2487.5     7162.5     2765.0     5343.0     4452.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     7083.0      774.0     5634.0     4638.0     6223.5     1114.5     3244.5 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     2200.5     3244.5      928.5     1866.5     6076.0     6618.5     6618.5 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     3244.5      416.5     5634.0     1596.0     4638.0     7031.5     5927.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     5927.0     6223.5     3244.5     5927.0     5343.0     4487.5     6448.5 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     4226.0     5770.0     5634.0     1596.0     4226.0     6448.5     4638.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     6738.5     5927.0     7031.5      561.5     7075.0     5501.5     6525.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     7075.0     2552.0      898.5     4419.5     6357.5      561.5     2552.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     5501.5     6558.5     6979.0     6051.0     6738.5     5634.0     3962.5 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     4487.5     6357.5     4852.0     5011.5      774.0     3766.5     1114.5 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     3244.5     4226.0     1114.5     3244.5     4638.0     1114.5     2765.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     3766.5      774.0     4638.0      774.0     2765.0     5343.0     1596.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     5634.0     6840.0     2487.5     5792.5     3244.5     6840.0     1596.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     5634.0      928.5     3998.5     2200.5     3244.5     2200.5     4226.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     5634.0     1114.5     3244.5     5011.5     2200.5     4226.0     5927.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##      416.5     2200.5      774.0     4226.0     4226.0     2200.5     5634.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     2200.5     5011.5     2612.0     6697.0     2552.0     4487.5     6379.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     1114.5     2765.0     4638.0     5170.0     3244.5     7031.5     5190.5 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     6223.5     6448.5     3482.5     6887.5     3244.5     5343.0      416.5 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     5634.0       85.5     5634.0     2200.5     5927.0     6448.5     2989.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     2989.0     1956.5     4044.0     4226.0     5011.5     5011.5     5634.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     2200.5      416.5     2200.5     4638.0     6738.5     6738.5     7178.5 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     6738.5     5927.0     7312.0     2200.5     3244.5     3244.5     5927.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     2200.5     2200.5     6618.5     5011.5     5011.5     7031.5     6223.5 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     1596.0     3244.5     5011.5     5011.5     3766.5     5011.5     6223.5 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     6223.5     6223.5     1402.0     2989.0     4822.5     4822.5     1596.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##      416.5     4226.0     2200.5      774.0     4638.0     7178.5     2913.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     4419.5     6357.5     6887.5     4452.0     4452.0     6051.0     4791.5 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     4226.0     6223.5     2765.0     5011.5     5634.0     6840.0     6618.5 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     6618.5     1114.5     1114.5     3766.5     3766.5     1297.0     2913.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     4767.5     2200.5     3766.5     5343.0      774.0     3244.5     3766.5 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     3766.5     6223.5     6558.5       85.5     1596.0     2765.0     5634.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##      416.5     1114.5     2200.5      192.0      416.5     1596.0     5634.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     5927.0     5634.0     6840.0     6618.5     5634.0     5634.0     1114.5 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     6738.5     6738.5     2765.0     5343.0     4226.0     5927.0     3244.5 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     5927.0     5634.0     5634.0     4226.0     5634.0     6448.5     6840.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     7212.0      774.0     1596.0     2765.0     1114.5     2765.0     4226.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     4226.0     6223.5       85.5       85.5      774.0      774.0     1596.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     1596.0     5634.0     5634.0     6448.5     6448.5       35.0       35.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##      416.5      416.5      416.5     1596.0     4226.0     3244.5     2765.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     4638.0     6618.5     1596.0     4226.0      192.0      192.0     2200.5 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     2200.5     1114.5     4226.0     5011.5     5927.0     6618.5     7031.5 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     4226.0     1343.0     3962.5     2200.5     4638.0     6223.5     7212.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     1114.5     2765.0     1596.0      774.0     1596.0     1596.0     5476.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     4767.5      416.5      774.0     3244.5     3244.5      192.0     1114.5 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     2200.5     3244.5     5634.0     6223.5     3244.5     5343.0     3556.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     3998.5     5343.0     6738.5     5634.0     6223.5     6448.5     5927.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     6448.5     5809.5     5343.0     6448.5     6618.5     5011.5     5170.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     5170.0     3244.5     1866.5     2765.0     4638.0     3766.5     7031.5 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     3766.5     1114.5      774.0     2765.0     2765.0     5011.5     7031.5 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     7212.0      416.5     5343.0     5927.0     6223.5     6223.5     5634.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     5634.0     2200.5     4226.0     5011.5     7031.5     7031.5     7147.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     6223.5     1596.0     3244.5     5011.5     3766.5     5011.5     6223.5 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     1402.0     2989.0     4822.5       85.5     5634.0     2200.5     4226.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     2765.0     5343.0      416.5     1114.5     7031.5     3962.5     3962.5 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     3244.5       35.0       35.0       85.5     1114.5     4226.0     5927.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##      774.0     1596.0     2765.0     1596.0     2200.5     1114.5     2200.5 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##      416.5     4226.0     2200.5     5011.5     6618.5     5343.0      416.5 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##      416.5     5927.0       35.0      774.0     6618.5     3766.5     5770.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##      416.5      416.5     5343.0     1866.5      642.0     5343.0       35.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     1114.5     1596.0     5343.0      416.5      416.5      416.5     1596.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     5343.0     3766.5     4638.0        5.0     5343.0     4638.0     6738.5 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     1114.5     2765.0       85.5      416.5     2200.5     2200.5      774.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##      416.5     3766.5     1596.0     4638.0     4226.0     4226.0     3766.5 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     5927.0     6738.5     5343.0     1114.5     2200.5     3244.5     5343.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     4638.0      774.0     4226.0     3766.5     5927.0     5213.5     6223.5 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     2765.0     6738.5     6223.5     5343.0     1297.0     2437.5     3482.5 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     4419.5     6618.5     5634.0     6223.5     4226.0     5927.0     6840.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     7178.5     1596.0     2765.0     5011.5     6223.5     1114.5     2200.5 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     3244.5     4226.0      774.0     4226.0     3244.5     3244.5     6223.5 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     6223.5       85.5       35.0     3766.5       35.0     3766.5      416.5 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     2200.5     4226.0      416.5     3244.5     5011.5     5343.0     6940.5 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     5343.0     3766.5     1114.5     2200.5     5011.5     6223.5     3766.5 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     5011.5      774.0     4226.0      416.5     5634.0     6223.5     1114.5 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     7254.0     1114.5      275.0     1866.5     3998.5      192.0     2200.5 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##       85.5      416.5     2200.5     5634.0     2200.5       85.5     2200.5 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     2200.5      416.5     1114.5     2200.5     2200.5     3244.5     5011.5 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     6223.5     3766.5     6448.5     7104.0     5927.0     5927.0     5343.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     5343.0     3766.5     1114.5     2765.0     3766.5     5927.0     6448.5 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     3244.5     3244.5      774.0     1114.5       85.5       85.5     3244.5 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     5011.5     3244.5       15.5     4226.0     6223.5      774.0     2765.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     2200.5     5011.5     5011.5     5634.0     3766.5     4226.0     4226.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     3244.5     3244.5      774.0     2765.0     2765.0     4638.0      141.5 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     1402.0     4638.0     1596.0     5011.5     5170.0     4226.0     4226.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     4226.0     4226.0     1114.5     1596.0     3766.5     4638.0      192.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##      192.0     2437.5     3766.5     4638.0     6738.5      141.5     2200.5 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     5011.5     6223.5      192.0     1596.0     3766.5      603.5     2487.5 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     2487.5     2765.0     6051.0     2200.5     2200.5     6223.5     6840.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##      416.5     2200.5     4226.0     6618.5     2200.5     2200.5     2200.5 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     6618.5     6618.5     6618.5     6618.5     5634.0     5634.0     5634.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     5634.0     5927.0     6738.5     6223.5     7212.0     7212.0     5634.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     7287.5      561.5      870.0     2913.0     3482.5     5170.0     6543.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     1114.5     2200.5     3244.5     3244.5     5011.5     6223.5      561.5 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     4419.5      192.0     3766.5     1596.0     3244.5     4226.0     2765.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     5634.0     2989.0     2989.0      416.5      416.5      870.0      870.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##      870.0      870.0     3935.0     4226.0     1114.5     5927.0     5343.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     6738.5      774.0     5343.0      678.5      958.0     6738.5     4226.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     5011.5     5343.0     6448.5     7104.0     6448.5     4419.5     5343.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     6681.5     1596.0     4510.5     5233.5      141.5      642.0       15.5 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     3244.5      416.5     5770.0      774.0     1343.0     5190.5     6369.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     5792.5        5.0     3244.5     5634.0     6681.5      603.5     1796.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     3766.5     5927.0     2200.5     5343.0     1751.0     3935.0     5770.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     2200.5     1114.5     3244.5      416.5     6940.5     3244.5     2200.5 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     1114.5     2200.5     5927.0     5213.5     5011.5     5927.0     5927.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     3766.5     5927.0     5011.5     5634.0     5011.5     6618.5     1114.5 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     1596.0     6618.5     5770.0     5458.0     5458.0     5458.0     5458.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     5458.0     5927.0     5011.5     6738.5     5634.0     6223.5     4791.5 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     6223.5     5634.0     5634.0     5343.0     5011.5     6223.5     6940.5 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     6223.5     6979.0     6840.0     6223.5     3556.0     5213.5     6690.5 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     1596.0     2765.0     3766.5     1596.0     3244.5     4226.0     1343.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     4791.5     2765.0     3244.5     5011.5     3935.0     6509.0     3595.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     6357.5     3595.0     6357.5     3595.0     6357.5      151.5      305.5 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     1402.0     3766.5     5809.5     4767.5     6781.0       35.0       85.5 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##      192.0     4452.0     6697.0     3998.5     5488.0     2200.5     2200.5 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##      774.0     1114.5     1596.0     3595.0     4044.0     4510.5     1596.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     5343.0     1343.0     1343.0     5792.5     2940.0     1796.0     6681.5 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     3935.0     6781.0      416.5     5634.0     6076.0     6076.0     3766.5 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     4510.5     5792.5     2200.5     2200.5     5927.0     2765.0     5927.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     7104.0     3244.5     2765.0     1596.0     1866.5     2989.0     6051.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##      416.5      416.5      416.5      131.5      603.5     4226.0     6618.5 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     3766.5     4638.0     3766.5     6448.5     4638.0     6738.5     1596.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     2765.0     3766.5       85.5      416.5      416.5     2765.0     6076.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     1446.5     3051.0     4852.0     4638.0     5927.0     6379.0     3244.5 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##       85.5       35.0      416.5     1114.5     3244.5     3766.5     3766.5 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     6543.0       15.5       85.5      416.5     2200.5     5634.0     3244.5 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     1114.5     2200.5      416.5     1114.5       85.5     2765.0     5011.5 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     2940.0     3962.5     6051.0     1114.5     2200.5     6448.5      192.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     7178.5      603.5      603.5     3766.5     6448.5     1866.5      774.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     2765.0     3244.5     4226.0     2765.0     3244.5     5011.5     3244.5 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     5011.5     3766.5     6558.5      928.5     2989.0     6076.0     3244.5 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     6223.5     6543.0     6543.0     6543.0     6673.5     6887.5     6673.5 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     6357.5     5634.0     5634.0     6357.5     5770.0     5927.0     5927.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     5927.0     6543.0     6543.0     5634.0     6985.0     6092.5      928.5 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     2940.0     3595.0     3244.5     4487.5     6389.5     4419.5     6051.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     6895.5     7083.0     2940.0     4822.5     3517.0     5233.5     6972.5 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##      774.0      305.5     5501.5     4452.0     6509.0     1343.0     2487.5 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     4638.0     2487.5     3998.5     1402.0     2989.0     6369.0     3998.5 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     6389.5     6389.5      275.0      774.0     3244.5     3244.5      141.5 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##      305.5     1956.5     4452.0     1596.0     4487.5     2200.5     3998.5 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##      416.5     1446.5     1446.5     3051.0     4226.0     6558.5     1866.5 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     4419.5     1751.0     2989.0     3244.5     6792.5     6357.5     2200.5 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     1114.5     3244.5     5011.5     4638.0      928.5     2913.0     2437.5 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     2913.0     1751.0     1297.0     5233.5     6092.5     4044.0     2612.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##      275.0      275.0     2765.0     6904.0     6092.5     6092.5     5927.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     6738.5     5343.0     2200.5     6618.5     4226.0     5011.5     4226.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     1343.0     2487.5     4226.0     4226.0     5634.0     3244.5     1114.5 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     2200.5     3244.5     3766.5     4638.0     3244.5     1596.0     1596.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     1751.0      870.0     1402.0     3556.0     1402.0     5809.5     5809.5 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     5809.5     5809.5     5233.5     6910.0     3244.5     3244.5     6681.5 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     5190.5     6618.5     1114.5      416.5     1596.0      774.0     1596.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##      774.0      252.5     1343.0      774.0     1596.0       35.0      416.5 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     4791.5     6788.0     2200.5     3244.5      603.5     3517.0     4452.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     6223.5     3244.5     5011.5     5011.5     1114.5       35.0      774.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##        5.0     3051.0     5927.0     3244.5     6840.0     3766.5      870.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     6035.0     6035.0     5792.5      870.0     1114.5     1596.0     3766.5 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     5343.0     7031.5     5927.0     4638.0     5343.0      898.5      898.5 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     6051.0     6051.0     6379.0     4852.0     7233.5     5213.5     3482.5 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     6558.5     1402.0     4852.0     4791.5     4791.5     6990.5     3766.5 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     6940.5     4487.5     6887.5       23.0     6525.0     7165.0      678.5 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##      678.5     3766.5     5011.5     5343.0     4044.0     5818.0     6795.5 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     4226.0     5634.0     6840.0      192.0       85.5     2940.0     4791.5 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     3962.5     1596.0      416.5     5634.0     2200.5     5634.0       15.5 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     3244.5     1866.5     5488.0     1446.5     4510.5      233.0     6887.5 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##      416.5     2200.5      416.5       85.5     4226.0      416.5     1751.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     4419.5     5343.0     3935.0     7120.0     1114.5     4638.0     4638.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     1596.0     3766.5      603.5     1343.0     2200.5     4226.0      958.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     1956.5      305.5     2200.5     4226.0     3244.5     5927.0     2200.5 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##      416.5     3766.5     4226.0      416.5      678.5     3595.0     4638.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##       35.0       85.5     3766.5     4226.0     2200.5     7031.5     5927.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     5343.0     6051.0     6051.0     1114.5      416.5     4638.0     2765.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     4638.0     2765.0     5343.0     5190.5     6369.0     7162.5     7191.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     7129.0     6985.0     7129.0     7031.5     7031.5     4452.0     6051.0 
##       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>       <NA> 
##     6051.0     6448.5     7301.0     5011.5      416.5     7031.5     5011.5 
##       <NA>       <NA> 
##     2200.5     7212.0
rank(d2$HP)
##    [1]  156.0  390.0  754.5  754.5   63.5  336.0  714.0  714.0  714.0  129.0
##   [11]  344.5  724.0  724.0  156.0  231.5  390.0   88.5  156.0  483.5  483.5
##   [21]   88.5  447.0  786.5  786.5   23.5   23.5  305.0  669.5   88.5  483.5
##   [31]   41.0  390.0   41.0  156.0  390.0  390.0  231.5  231.5  669.5  669.5
##   [41]  305.0  576.0  843.5  181.0  434.5  782.0  576.0  891.0   57.0   57.0
##   [51]  632.5  632.5 1005.0 1029.5   88.5  669.5  156.0  390.0  669.5   41.0
##   [61]  390.0  390.0  576.0    2.5    2.5   41.0   41.0   88.5   88.5  231.5
##   [71]  483.5  483.5  231.5  754.5   88.5  483.5  305.0  843.5   88.5  483.5
##   [81]  843.5   11.5   88.5  305.0  305.0  576.0  754.5  843.5  231.5  483.5
##   [91]  754.5   88.5  754.5   88.5   88.5  305.0  305.0  754.5  754.5  231.5
##  [101]  231.5  483.5  483.5  843.5  843.5  891.0  891.0  891.0   11.5  231.5
##  [111]  274.0  274.0   41.0  390.0  483.5  843.5  754.5  754.5  970.5  970.5
##  [121]   23.5  231.5   23.5  156.0  390.0  390.0   41.0  390.0  801.5   23.5
##  [131]  305.0   88.5  390.0  390.0  891.0  891.0  231.5  390.0  390.0  231.5
##  [141]  231.5  843.5   88.5  483.5  483.5  754.5  970.5 1043.0  483.5  970.5
##  [151]  970.5   23.5  305.0  156.0  754.5   23.5  390.0   88.5  231.5  576.0
##  [161]  483.5  483.5  483.5  483.5  483.5  669.5    6.5  891.0  891.0 1023.0
##  [171]  187.5  305.0  483.5 1023.0  483.5  483.5  483.5   41.0  576.0   23.5
##  [181]  390.0  754.5  754.5 1036.0  843.5  843.5  843.5  843.5  843.5  843.5
##  [191]  114.5  434.5  867.0  980.0  980.0  980.0  936.5  156.0  390.0  754.5
##  [201]   63.5  336.0  714.0  231.5  483.5  801.5   41.0  801.5  390.0  936.5
##  [211]   88.5  305.0   88.5  576.0  801.5  669.5 1017.5    6.5  231.5  843.5
##  [221]   41.0  305.0   88.5  483.5  305.0  576.0  843.5  843.5  669.5  576.0
##  [231]  936.5  576.0  843.5   41.0  305.0  669.5  305.0   23.5  669.5  483.5
##  [241]  305.0  891.0  483.5  891.0  390.0  891.0  891.0  390.0  187.5 1039.0
##  [251]  576.0  231.5  669.5  936.5  483.5  669.5  669.5  390.0  843.5  483.5
##  [261]  576.0  576.0    6.5  754.5  754.5  305.0  390.0  843.5   88.5  390.0
##  [271]  231.5  936.5  483.5  390.0   41.0  669.5  156.0  801.5  483.5  156.0
##  [281]  669.5  669.5  669.5  843.5  843.5  801.5  632.5  305.0   41.0  231.5
##  [291]  156.0  156.0  156.0  891.0 1044.5  843.5 1005.0  936.5  231.5  576.0
##  [301]  936.5  936.5  980.0  980.0  936.5   88.5  231.5  576.0  576.0  156.0
##  [311]  390.0  754.5  754.5  231.5  576.0  936.5  936.5   41.0  576.0   57.0
##  [321]   57.0  714.0  714.0  156.0  231.5  390.0  231.5  390.0   88.5  390.0
##  [331]  754.5   88.5  576.0  843.5   88.5  390.0   88.5  390.0   14.5   57.0
##  [341]  528.0  528.0   88.5  576.0  390.0  390.0  390.0  754.5 1033.5   32.0
##  [351]  434.5    1.0  451.5  789.5  963.0  624.5 1031.0  231.5   23.5  231.5
##  [361]  576.0  231.5  231.5  231.5  231.5  231.5  390.0  576.0  576.0   23.5
##  [371]  390.0  390.0   88.5  576.0  576.0  390.0  390.0  483.5  483.5  231.5
##  [381]  576.0  936.5  156.0  576.0  576.0 1023.0 1038.0  390.0  576.0  576.0
##  [391]  576.0  390.0  754.5  390.0  156.0  231.5  754.5  231.5  576.0  156.0
##  [401]  669.5  669.5  632.5  632.5  843.5  843.5  231.5  996.0  123.0  447.0
##  [411]   88.5  390.0  514.0  814.5  156.0  669.5    6.5  891.0  576.0  576.0
##  [421]  576.0  576.0  390.0  129.0  451.5  451.5    6.5   88.5  914.0  669.5
##  [431]  483.5  483.5  891.0  231.5  754.5  754.5  576.0  843.5  996.0   41.0
##  [441]  305.0  305.0  936.5  123.0  156.0  483.5  891.0  891.0   88.5  390.0
##  [451]  754.5  754.5  754.5  754.5  754.5  754.5  754.5  754.5  754.5  936.5
##  [461]  936.5  936.5  936.5  970.5  970.5  936.5  231.5  231.5  231.5  231.5
##  [471]  305.0  669.5  891.0  129.0  451.5  701.0  278.0  451.5  789.5   88.5
##  [481]  305.0  801.5  344.5  724.0   51.0  705.5  156.0  390.0  754.5   88.5
##  [491]  390.0  518.5  909.0   23.5  390.0   88.5  390.0  390.0  390.0  576.0
##  [501]   23.5  576.0  390.0  305.0  801.5  156.0  576.0  701.0 1002.0  669.5
##  [511]  843.5 1033.5  305.0  483.5  483.5  390.0  936.5  192.0  617.0  156.0
##  [521]  447.0  961.0  329.0  518.5  231.5    6.5  936.5  701.0  231.5  336.0
##  [531]  528.0  985.5  985.5 1025.5   88.5  576.0  576.0  528.0  985.5   88.5
##  [541]  576.0  187.5  786.5  638.0  192.0  536.0  156.0  390.0  843.5  843.5
##  [551]  576.0  576.0  996.0 1005.0  936.5  669.5  669.5  801.5  814.5  483.5
##  [561]  483.5  669.5  996.0  801.5  528.0  528.0  390.0  156.0  576.0  231.5
##  [571]  231.5  231.5  231.5  231.5  231.5  669.5  754.5  669.5  936.5  843.5
##  [581]  867.0  996.0 1033.5 1033.5 1010.0  754.5  936.5  576.0  936.5  936.5
##  [591] 1010.0  936.5  156.0  390.0  669.5  483.5  843.5  996.0  305.0  669.5
##  [601]  891.0  156.0  390.0  156.0  483.5  801.5  114.5  451.5  231.5  669.5
##  [611]  231.5  669.5  231.5  669.5  701.0 1007.0  231.5  441.5  754.5  156.0
##  [621]  669.5  305.0  576.0  801.5  483.5  518.5  390.0  996.0  961.0  961.0
##  [631]  669.5  801.5  970.5  231.5  669.5  970.5 1010.0  669.5  156.0  305.0
##  [641]  669.5   23.5   88.5  390.0   88.5  390.0  156.0  576.0  576.0  576.0
##  [651]  231.5  390.0  891.0  576.0  576.0  970.5  970.5  970.5  970.5  669.5
##  [661]  231.5  576.0  231.5  483.5  624.5   57.0   57.0  336.0  282.0  638.0
##  [671]  305.0  669.5  231.5  754.5   88.5  390.0  305.0  669.5  156.0  390.0
##  [681]  576.0  156.0  483.5  996.0  441.5  669.5   50.0  270.5  617.0  390.0
##  [691]  754.5  305.0  231.5  576.0  536.0 1003.0  305.0  936.5 1037.0  231.5
##  [701]  576.0  129.0  638.0   88.5  390.0  390.0   41.0  483.5  801.5  305.0
##  [711]  669.5  231.5  390.0  390.0  181.0  514.0  701.0  305.0  891.0  754.5
##  [721]  231.5  754.5  989.0  989.0  156.0  483.5  705.5  344.5  822.0  156.0
##  [731]  483.5  891.0  576.0  936.5  576.0  996.0  801.5  336.0  274.0  624.5
##  [741]  873.0  305.0  801.5  867.0  867.0  867.0  724.0  724.0  724.0  724.0
##  [751]  936.5  936.5  822.0  822.0 1017.5 1017.5 1017.5  867.0  867.0  936.5
##  [761]  936.5  617.0  326.0  434.5  818.5   88.5  344.5  669.5  114.5  282.0
##  [771]  624.5  624.5   57.0  801.5  156.0  441.5  714.0   57.0  156.0  754.5
##  [781]  441.5  814.5  129.0  282.0  714.0  514.0 1014.5  518.5  891.0  669.5
##  [791]  441.5  638.0  638.0  156.0  344.5  390.0  390.0  714.0  959.0  441.5
##  [801]  783.5  278.0  814.5  118.5  624.5  231.5  483.5  231.5  617.0  129.0
##  [811]  441.5  336.0  783.5  705.5 1014.5  891.0  714.0  518.5  231.5  156.0
##  [821]  528.0  843.5  329.0  123.0  801.5  192.0  129.0  282.0  344.5  483.5
##  [831]  305.0  669.5  801.5  305.0  891.0   88.5  801.5 1020.5 1020.5  985.5
##  [841]  282.0 1041.0  231.5  231.5  754.5  754.5  754.5  528.0  714.0  714.0
##  [851]  156.0  483.5  891.0  231.5  390.0  754.5   41.0  305.0  754.5  187.5
##  [861]  818.5  183.5  329.0  705.5  183.5  909.0  669.5  669.5  669.5  669.5
##  [871]   88.5  390.0  156.0  156.0  669.5  801.5  669.5  156.0  156.0  231.5
##  [881]  231.5  576.0  936.5   57.0  528.0   88.5  576.0   88.5  390.0  187.5
##  [891]  528.0  576.0 1010.0  118.5  274.0  624.5  270.5  843.5  936.5   11.5
##  [901]  669.5  305.0  801.5  305.0  891.0  891.0  390.0  390.0  483.5  390.0
##  [911]  483.5  305.0  528.0  714.0  576.0  156.0  305.0  669.5  576.0  576.0
##  [921]  576.0  576.0  123.0  123.0 1027.5 1027.5  989.0  983.0  617.0  786.5
##  [931]  909.0  344.5 1042.0  909.0  909.0  909.0  909.0  754.5  843.5  518.5
##  [941]  632.5  434.5  278.0  818.5  181.0 1025.5  231.5  576.0  936.5  231.5
##  [951]  483.5  754.5  231.5  483.5  576.0  576.0 1010.0   57.0  528.0  913.0
##  [961]   11.5  231.5  390.0   88.5  576.0   88.5  390.0  118.5  624.5  231.5
##  [971]  843.5  344.5  536.0   23.5  754.5  996.0   88.5  576.0  996.0  274.0
##  [981]  624.5  576.0  114.5  434.5   88.5  669.5  669.5  231.5  936.5  231.5
##  [991]  754.5   88.5  390.0  118.5  329.0  329.0  156.0  483.5  891.0  876.0
## [1001]  576.0  390.0  441.5  754.5  336.0  156.0  483.5  483.5  187.5   23.5
## [1011]  576.0  936.5  669.5  669.5  390.0  576.0  336.0  336.0  624.5 1013.0
## [1021]  843.5  843.5  843.5  843.5  576.0   14.5  528.0  818.5  873.0  873.0
## [1031]  873.0  873.0 1029.5 1044.5  390.0  936.5  936.5  970.5  754.5 1040.0
## [1041]  936.5  936.5  936.5  936.5  936.5
head(d2[5])
with(d2, sum(Defence))
## [1] 78021
sd(d2$HP)
## [1] 26.67141

Summarizing rows and columns:

mad(d2$Defence)
## [1] 29.652
apply(head(d2),1, mean, na.rm=TRUE)
## Warning in mean.default(newX[, i], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(newX[, i], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(newX[, i], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(newX[, i], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(newX[, i], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(newX[, i], ...): argument is not numeric or logical:
## returning NA
##  1  2  3  4  5  6 
## NA NA NA NA NA NA
apply(head(d2),1, median, na.rm=TRUE)
## Warning in mean.default(sort(x, partial = half + 0L:1L)[half + 0L:1L]): argument
## is not numeric or logical: returning NA
## Warning in mean.default(sort(x, partial = half + 0L:1L)[half + 0L:1L]): argument
## is not numeric or logical: returning NA

## Warning in mean.default(sort(x, partial = half + 0L:1L)[half + 0L:1L]): argument
## is not numeric or logical: returning NA

## Warning in mean.default(sort(x, partial = half + 0L:1L)[half + 0L:1L]): argument
## is not numeric or logical: returning NA

## Warning in mean.default(sort(x, partial = half + 0L:1L)[half + 0L:1L]): argument
## is not numeric or logical: returning NA

## Warning in mean.default(sort(x, partial = half + 0L:1L)[half + 0L:1L]): argument
## is not numeric or logical: returning NA
##  1  2  3  4  5  6 
## NA NA NA NA NA NA
is.table(d2)
## [1] FALSE
addmargins(head(d2),1, mean)
## Warning in mean.default(newX[, i], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(newX[, i], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(newX[, i], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(newX[, i], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(newX[, i], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(newX[, i], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(newX[, i], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(newX[, i], ...): argument is not numeric or logical:
## returning NA
##      Name        Total       HP          Attack    Defence     Sp_attack  
## 1    character,6 integer,6   integer,6   integer,6 character,6 integer,6  
## 2    integer,6   integer,6   integer,6   integer,6 integer,6   integer,6  
## 3    integer,6   character,6 integer,6   integer,6 integer,6   character,6
## 4    integer,6   integer,6   integer,6   integer,6 integer,6   integer,6  
## 5    integer,6   integer,6   character,6 integer,6 integer,6   integer,6  
## 6    integer,6   integer,6   integer,6   integer,6 integer,6   integer,6  
## mean NA          NA          NA          NA        NA          NA         
##      Sp_defence  Speed    
## 1    integer,6   integer,6
## 2    integer,6   integer,6
## 3    integer,6   integer,6
## 4    integer,6   integer,6
## 5    character,6 integer,6
## 6    integer,6   integer,6
## mean NA          NA
addmargins(head(d2),1, median)
## Warning in mean.default(sort(x, partial = half + 0L:1L)[half + 0L:1L]): argument
## is not numeric or logical: returning NA
## Warning in mean.default(sort(x, partial = half + 0L:1L)[half + 0L:1L]): argument
## is not numeric or logical: returning NA

## Warning in mean.default(sort(x, partial = half + 0L:1L)[half + 0L:1L]): argument
## is not numeric or logical: returning NA

## Warning in mean.default(sort(x, partial = half + 0L:1L)[half + 0L:1L]): argument
## is not numeric or logical: returning NA

## Warning in mean.default(sort(x, partial = half + 0L:1L)[half + 0L:1L]): argument
## is not numeric or logical: returning NA

## Warning in mean.default(sort(x, partial = half + 0L:1L)[half + 0L:1L]): argument
## is not numeric or logical: returning NA

## Warning in mean.default(sort(x, partial = half + 0L:1L)[half + 0L:1L]): argument
## is not numeric or logical: returning NA

## Warning in mean.default(sort(x, partial = half + 0L:1L)[half + 0L:1L]): argument
## is not numeric or logical: returning NA
##        Name        Total       HP          Attack    Defence     Sp_attack  
## 1      character,6 integer,6   integer,6   integer,6 character,6 integer,6  
## 2      integer,6   integer,6   integer,6   integer,6 integer,6   integer,6  
## 3      integer,6   character,6 integer,6   integer,6 integer,6   character,6
## 4      integer,6   integer,6   integer,6   integer,6 integer,6   integer,6  
## 5      integer,6   integer,6   character,6 integer,6 integer,6   integer,6  
## 6      integer,6   integer,6   integer,6   integer,6 integer,6   integer,6  
## median NA          NA          NA          NA        NA          NA         
##        Sp_defence  Speed    
## 1      integer,6   integer,6
## 2      integer,6   integer,6
## 3      integer,6   integer,6
## 4      integer,6   integer,6
## 5      character,6 integer,6
## 6      integer,6   integer,6
## median NA          NA
View(head(d2))
nrow((d2))
## [1] 1045
ncol(d2)
## [1] 8
sum(is.na(d2))
## [1] 0
structure(head(d2))
table(head((d2$Speed)))
## 
## 45 60 65 80 
##  1  1  1  3
plot(density(d2$HP))

Drawing distribution and Graphics:

hist(d2$HP, freq = F, col= 'blue')
lines(density(d2$HP), lty= 2)
lines(density(d2$HP, k='rectangular'))

rnorm(20, mean=5, sd= 1)
##  [1] 6.245765 4.626042 4.520046 4.514566 5.927319 6.301246 2.593641 5.759398
##  [9] 5.981533 4.831372 4.678042 5.047421 5.289058 5.060538 5.263293 3.713043
## [17] 6.305507 4.501304 7.100012 3.677507
pnorm(20, mean=5, sd= 1)
## [1] 1
qnorm(0.5, 5, 1)
## [1] 5
dnorm(c(7,8,9),mean=5, sd=1)
## [1] 0.0539909665 0.0044318484 0.0001338302
qnorm(c(3.5,0.75),mean=5, sd=1)
## Warning in qnorm(c(3.5, 0.75), mean = 5, sd = 1): NaNs produced
## [1]     NaN 5.67449
d2.norm=rnorm(1000,mean(d2$HP), sd(d2$HP))
head(d2.norm)
## [1]  3.433238 71.627281 80.308037 53.819142 91.454385 36.789027
hist(d2$HP, freq = F, col= 'pink')
lines(density(d2.norm))

hist(d2.norm, freq = F, col= 'pink')
lines(density(d2$HP))

hist(d2.norm,freq = F,border = 'red',col='blue',main = 'comparing two distributions',xlab = 'data size classes')
lines(density(d2$HP), lwd= 2)

qqnorm(d2$HP)

qqnorm(d2$HP, main = 'QQ plot of example data', xlab = 'Theoretical',ylab = 'Quantiles for d2')
qqline(d2$HP, lwd = 2, lty = 2)

qqp = qqplot(d2$HP, rnorm(50,5,2))
qqp
## $x
##  [1]   1.00000  30.00000  35.00000  39.91837  40.00000  40.00000  44.00000
##  [8]  45.00000  45.00000  49.00000  50.00000  50.00000  50.00000  53.00000
## [15]  55.00000  55.00000  59.00000  60.00000  60.00000  60.00000  60.00000
## [22]  63.42857  65.00000  65.00000  65.34694  68.00000  70.00000  70.00000
## [29]  70.00000  71.00000  74.18367  75.00000  75.00000  77.00000  79.00000
## [36]  80.00000  80.00000  84.00000  85.00000  90.00000  90.00000  92.00000
## [43]  95.00000 100.00000 100.00000 102.55102 106.00000 112.16327 130.00000
## [50] 255.00000
## 
## $y
##  [1] 0.8725173 1.2141105 1.5190249 2.2361928 2.2758667 2.2897934 2.4174826
##  [8] 2.5086645 2.8454379 2.8997559 2.9381949 3.0573228 3.2976195 3.3351876
## [15] 3.6054571 3.8733521 4.0246310 4.0515806 4.2754768 4.3279907 4.4082356
## [22] 4.6557145 4.7224334 4.8894435 4.9479609 5.0230373 5.0250852 5.0476994
## [29] 5.4301908 5.4598904 5.5486123 5.5859593 5.7391962 5.7503352 5.7591449
## [36] 5.7616949 5.8550595 5.9131315 6.0106015 6.0210990 6.3573668 6.5285704
## [43] 6.5909674 6.7343640 6.7453835 7.6630885 7.7153135 8.1931636 8.3480016
## [50] 9.9125209
abline(lm(qqp$y ~ qqp$x))

Testing distribution:

sample(head(d2),size = 4)
set.seed(4)
sample(head(df), size = 3)
## [1]     if (missing(ncp))                       
## [2] }                                           
## [3]     else .Call(C_dnf, x, df1, df2, ncp, log)
shapiro.test(d2$HP)
## 
##  Shapiro-Wilk normality test
## 
## data:  d2$HP
## W = 0.89348, p-value < 2.2e-16
ks.test(d2$HP, 'pnorm',mean=5,sd=2)
## Warning in ks.test(d2$HP, "pnorm", mean = 5, sd = 2): ties should not be present
## for the Kolmogorov-Smirnov test
## 
##  One-sample Kolmogorov-Smirnov test
## 
## data:  d2$HP
## D = 0.99713, p-value < 2.2e-16
## alternative hypothesis: two-sided
ks.test(d2$HP, pnorm(20,5,2))
## Warning in ks.test(d2$HP, pnorm(20, 5, 2)): cannot compute exact p-value with
## ties
## 
##  Two-sample Kolmogorov-Smirnov test
## 
## data:  d2$HP and pnorm(20, 5, 2)
## D = 1, p-value = 0.2705
## alternative hypothesis: two-sided

T-test:

t.test(d2$HP, d2$Attack)
## 
##  Welch Two Sample t-test
## 
## data:  d2$HP and d2$Attack
## t = -8.0084, df = 2013.4, p-value = 1.946e-15
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
##  -12.945611  -7.852475
## sample estimates:
## mean of x mean of y 
##  70.06794  80.46699
t.test(d2$HP, d2$Attack, var.equal = TRUE)
## 
##  Two Sample t-test
## 
## data:  d2$HP and d2$Attack
## t = -8.0084, df = 2088, p-value = 1.911e-15
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
##  -12.94556  -7.85253
## sample estimates:
## mean of x mean of y 
##  70.06794  80.46699
t.test(d2$HP, mu = 10)
## 
##  One Sample t-test
## 
## data:  d2$HP
## t = 72.804, df = 1044, p-value < 2.2e-16
## alternative hypothesis: true mean is not equal to 10
## 95 percent confidence interval:
##  68.44897 71.68692
## sample estimates:
## mean of x 
##  70.06794
t.test(d2$HP, mu = 1000, alternative = 'greater')
## 
##  One Sample t-test
## 
## data:  d2$HP
## t = -1127.1, df = 1044, p-value = 1
## alternative hypothesis: true mean is greater than 1000
## 95 percent confidence interval:
##  68.70963      Inf
## sample estimates:
## mean of x 
##  70.06794

U-test:

wilcox.test(d2$HP, d2$Attack)
## 
##  Wilcoxon rank sum test with continuity correction
## 
## data:  d2$HP and d2$Attack
## W = 437371, p-value = 3.245e-15
## alternative hypothesis: true location shift is not equal to 0
wilcox.test(d2$HP, exact = FALSE)
## 
##  Wilcoxon signed rank test with continuity correction
## 
## data:  d2$HP
## V = 546535, p-value < 2.2e-16
## alternative hypothesis: true location is not equal to 0
wilcox.test(d2$HP, mu = 8, exact = FALSE, conf.int = TRUE, alt = 'less')
## 
##  Wilcoxon signed rank test with continuity correction
## 
## data:  d2$HP
## V = 546532, p-value = 1
## alternative hypothesis: true location is less than 8
## 95 percent confidence interval:
##  -Inf   70
## sample estimates:
## (pseudo)median 
##           67.5

Correlation and Covariance:

cor(d2$Defence, d2$Attack)
## [1] 0.4576705
cor(d2$Attack, d2$Defence, method = 'spearman')
## [1] 0.5167943
cor(d2$Attack, d2$Defence, method = 'kendall')
## [1] 0.3738551
cov(d2$Attack, d2$Defence)
## [1] 463.4074

Correlation hypothesis tests:

cor.test(d2$Attack, d2$Defence)
## 
##  Pearson's product-moment correlation
## 
## data:  d2$Attack and d2$Defence
## t = 16.624, df = 1043, p-value < 2.2e-16
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  0.4083614 0.5043165
## sample estimates:
##       cor 
## 0.4576705

Association tests:

chisq.test(d2$Attack)
## 
##  Chi-squared test for given probabilities
## 
## data:  d2$Attack
## X-squared = 13631, df = 1044, p-value < 2.2e-16
chisq.test(d2$HP, correct = FALSE)
## 
##  Chi-squared test for given probabilities
## 
## data:  d2$HP
## X-squared = 10599, df = 1044, p-value < 2.2e-16
d2.cs = chisq.test(d2$Attack, p = d2$Defence, rescale.p = TRUE)
d2.cs
## 
##  Chi-squared test for given probabilities
## 
## data:  d2$Attack
## X-squared = 14951, df = 1044, p-value < 2.2e-16
head(d2.cs$exp)
## [1]  52.81029  67.89895  89.45417 132.56462  46.34373  62.51014
names(d2.cs$expected) = row.names(d2)
head(d2.cs$exp)
##         1         2         3         4         5         6 
##  52.81029  67.89895  89.45417 132.56462  46.34373  62.51014
(honey.cs = chisq.test(d2$Attack))
## 
##  Chi-squared test for given probabilities
## 
## data:  d2$Attack
## X-squared = 13631, df = 1044, p-value < 2.2e-16
names(honey.cs)
## [1] "statistic" "parameter" "p.value"   "method"    "data.name" "observed" 
## [7] "expected"  "residuals" "stdres"
(honey.cs = chisq.test(d2$Attack, simulate.p.value = TRUE, B = 3000))
## 
##  Chi-squared test for given probabilities with simulated p-value (based
##  on 3000 replicates)
## 
## data:  d2$Attack
## X-squared = 13631, df = NA, p-value = 0.0003332
d2[1:2, 4:5]
chisq.test(d2[1:2, 4:5], correct = FALSE)
## 
##  Pearson's Chi-squared test
## 
## data:  d2[1:2, 4:5]
## X-squared = 0.0035158, df = 1, p-value = 0.9527
chisq.test(d2[1:2, 4:5], correct = TRUE)
## 
##  Pearson's Chi-squared test with Yates' continuity correction
## 
## data:  d2[1:2, 4:5]
## X-squared = 0, df = 1, p-value = 1
with(d2, chisq.test(Attack, p = Defence, rescale = T))
## 
##  Chi-squared test for given probabilities
## 
## data:  Attack
## X-squared = 14951, df = 1044, p-value < 2.2e-16
with(d2, chisq.test(Attack, p =Defence , rescale = T, sim = T))
## 
##  Chi-squared test for given probabilities with simulated p-value (based
##  on 2000 replicates)
## 
## data:  Attack
## X-squared = 14951, df = NA, p-value = 0.0004998
chisq.test(d2$Attack)
## 
##  Chi-squared test for given probabilities
## 
## data:  d2$Attack
## X-squared = 13631, df = 1044, p-value < 2.2e-16

Box-whisker plots:

boxplot(d2$Attack)

boxplot(d2$Attack, d2$Defence)

boxplot(d2$Attack, d2$Defence, names = c('Attack', 'Defence'))
title(xlab = 'Variable', ylab = 'Value')

boxplot(d2$Attack, d2$Defence, names = c('Attack', 'Defence'), range = 0,
xlab = 'Variable', ylab = 'Value', col = 'yellow')

boxplot(d2$Attack, d2$Defence, d2$HP, names = c('Attack', 'Defence', 'HP'),
range = 0, xlab = 'variable', ylab = 'value', col = 'pink')

Scatter plots:

plot(d2$Attack, d2$Defence, pch = "+")

plot(d2$Attack, d2$Defence, xlab = 'Attack', ylab = 'Defence', pch = 10, cex = 2, col = 'blue', xlim = c(0, 500), ylim = c(0, 500))

plot(Defence ~ Attack, data = d2)
abline(lm(Defence ~ Attack, data = d2))

plot(Defence ~ Attack, data = d2)
abline(lm(d2$Attack ~ d2$Defence, data = d2), lty = 'dotted', lwd = 2, col = 'green')

plot(d2$Attack ~ d2$Defence, data = d2, xlab = 'Attack', ylab = 'Defence',
pch = 10, cex = 2, col = 'gray50', xlim = c(0, 500), ylim = c(0, 500))
abline(lm(d2$Attack ~ d2$Defence, data = d2), lty = 'dotted', lwd = 2, col = 'blue')

plot(d2$Attack, type = 'l')

with(d2, plot(Attack, HP, type = 'c'))

with(d2[order(d2$Attack),], plot(sort(Attack), HP, type = 'c'))

plot(d2$Attack, type = 'b')

Multiple correlation plots:

pairs(~ Defence + Attack + HP, data = d2)

pairs(~ d2$Attack + d2$HP + d2$Defence, data = d2, col ='brown', cex = 5, pch = 'X')

Pie charts:

pie(d2$HP, labels = d2$Total)

 pc = c('gray40', 'gray50', 'gray60', 'gray70', 'gray80', 'gray90')
 pie(head(d2$HP, labels = d2$Attack, col = pc, clockwise = TRUE, init.angle = 180))

 pc = c('gray65', 'gray70', 'gray75', 'gray80', 'gray85', 'gray90')
 pie(head(d2$Attack, labels = row.names(d2), col = pc, cex = 1.2))

 d2[1,]

Cleveland dot charts:

dotchart(d2$Attack)

 dotchart(t(d2$HP))

 #Bar charts:

Bar charts:

barplot(d2$Attack)

 barplot(head(d2$Defence))

barplot(head(d2$Attack))
title(xlab = 'variable', ylab = 'value')

barplot(head(d2$Attack), xlab = 'variable', ylab = 'value', ylim = c(0,500),col = 'lightblue')
 abline(h = seq(1,9,2), lty = 2, lwd = 0.5, col = 'gray40')
 box()

 table(head(d2$Defence))
## 
##  43  49  58  63  83 123 
##   1   1   1   1   1   1
barplot(table(head(d2$Attack)), ylab = 'variable', xlab = 'value')
abline(h = 0)

barplot(head(d2$Attack, beside = TRUE, ylab = 'variable', xlab = 'value'))

barplot(t(head(d2$Attack)), beside = TRUE, legend = TRUE, cex.names = 0.8,col = c('black', 'pink', 'lightblue', 'tan', 'red', 'brown'))
title(ylab = 'variable', xlab = 'value')

bccol = c('black', 'pink', 'lightblue', 'tan', 'red', 'brown')
barplot(head(d2$Attack), beside = TRUE, legend = TRUE, horiz = TRUE,xlim = c(0, 60), col = bccol)
title(ylab = 'variable', xlab = 'vaule')

Analysis of variance (ANOVA):

bfs.aov = aov(d2$Attack ~ d2$Defence, data = d2)
bfs.aov
## Call:
##    aov(formula = d2$Attack ~ d2$Defence, data = d2)
## 
## Terms:
##                 d2$Defence Residuals
## Sum of Squares    229753.8  867120.3
## Deg. of Freedom          1      1043
## 
## Residual standard error: 28.83351
## Estimated effects may be unbalanced
summary(bfs.aov)
##               Df Sum Sq Mean Sq F value Pr(>F)    
## d2$Defence     1 229754  229754   276.4 <2e-16 ***
## Residuals   1043 867120     831                   
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
model.tables(bfs.aov, type = 'means')
## Warning in replications(paste("~", xx), data = mf): non-factors ignored:
## d2$Defence
## Tables of means
## Grand mean
##          
## 80.46699 
## 
##  d2$Defence 
## d2$Defence
##      5     10     15     20     23     25     28     30     31     32     33 
##  47.39  49.76  52.13  54.51  55.93  56.88  58.31  59.26  59.73  60.21  60.68 
##     34     35     37     38     39     40     41     42     43     44     45 
##  61.16  61.63  62.58  63.06  63.53  64.01  64.48  64.96  65.43  65.91  66.38 
##     47     48     49     50     51     52     53     54     55     56     57 
##  67.33  67.81  68.28  68.76  69.23  69.71  70.18  70.66  71.13  71.60  72.08 
##     58     59     60     61     62     63     64     65     66     67     68 
##  72.55  73.03  73.50  73.98  74.45  74.93  75.40  75.88  76.35  76.83  77.30 
##     69     70     71     72     73     74     75     76     77     78     79 
##  77.78  78.25  78.73  79.20  79.68  80.15  80.63  81.10  81.58  82.05  82.53 
##     80     82     83     84     85     86     87     88     89     90     91 
##  83.00  83.95  84.43  84.90  85.38  85.85  86.33  86.80  87.28  87.75  88.23 
##     92     94     95     97     98     99    100    101    102    103    105 
##  88.70  89.65  90.13  91.08  91.55  92.03  92.50  92.98  93.45  93.92  94.87 
##    106    107    108    109    110    111    112    115    116    118    119 
##  95.35  95.82  96.30  96.77  97.25  97.72  98.20  99.62 100.10 101.05 101.52 
##    120    121    122    123    125    126    127    129    130    131    133 
## 102.00 102.47 102.95 103.42 104.37 104.85 105.32 106.27 106.75 107.22 108.17 
##    135    139    140    143    145    150    152    160    168    180    184 
## 109.12 111.02 111.50 112.92 113.87 116.25 117.19 120.99 124.79 130.49 132.39 
##    200    211    230    250 
## 139.99 145.21 154.24 163.73
pw.aov = aov(d2$Attack ~ d2$Defence+ d2$Sp_attack, data = d2)
pw.aov = aov(d2$Attack ~d2$Defence * d2$Defence, data = d2)
pw.aov
## Call:
##    aov(formula = d2$Attack ~ d2$Defence * d2$Defence, data = d2)
## 
## Terms:
##                 d2$Defence Residuals
## Sum of Squares    229753.8  867120.3
## Deg. of Freedom          1      1043
## 
## Residual standard error: 28.83351
## Estimated effects may be unbalanced
pw.aov = aov(d2$Attack ~ d2$Defence + d2$Sp_attack + Attack:Defence, data = d2)
pw.aov
## Call:
##    aov(formula = d2$Attack ~ d2$Defence + d2$Sp_attack + Attack:Defence, 
##     data = d2)
## 
## Terms:
##                 d2$Defence d2$Sp_attack Attack:Defence Residuals
## Sum of Squares    229753.8      81979.6       607831.2  177309.5
## Deg. of Freedom          1            1              1      1041
## 
## Residual standard error: 13.05091
## Estimated effects may be unbalanced
summary(pw.aov)
##                  Df Sum Sq Mean Sq F value Pr(>F)    
## d2$Defence        1 229754  229754  1348.9 <2e-16 ***
## d2$Sp_attack      1  81980   81980   481.3 <2e-16 ***
## Attack:Defence    1 607831  607831  3568.6 <2e-16 ***
## Residuals      1041 177310     170                   
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
pw.aov = aov(d2$Attack ~ d2$Defence * d2$Sp_attack, data = d2)
boxplot(d2$Attack ~ d2$Defence * d2$Sp_attack,  data = d2, cex.axis =0.9)
title(xlab = 'varaible', ylab = 'value')

Interaction plots:

interaction.plot(d2$Attack, d2$Defence, d2$Sp_attack)

attach(d2)
## The following objects are masked from head(d2):
## 
##     Attack, Defence, HP, Name, Sp_attack, Sp_defence, Speed, Total
interaction.plot(d2$HP, d2$Attack, d2$Defence, type = 'b')

detach(d2)

interaction.plot(d2$HP, d2$Attack, d2$Defence, type = 'b', pch = 3:1)

interaction.plot(d2$HP, d2$Attack, d2$Defence, type = 'b', pch = 3:1, lty = 3:1,
col = c('red', 'blue', 'darkgreen'))

interaction.plot(d2$HP, d2$Attack, d2$Defence, type = 'b', pch = 3:1, fun = median)

interaction.plot(d2$HP, d2$Attack, d2$Defence, type = 'b', pch = 3:1,
xlab = 'variable', ylab = 'value',
main = 'Interaction plot')

head(replications(d2))
## Warning in replications(d2): non-factors ignored: Total
## Warning in replications(d2): non-factors ignored: HP
## Warning in replications(d2): non-factors ignored: Attack
## Warning in replications(d2): non-factors ignored: Defence
## Warning in replications(d2): non-factors ignored: Sp_attack
## Warning in replications(d2): non-factors ignored: Sp_defence
## Warning in replications(d2): non-factors ignored: Speed
## $Name
## Name
##         Abomasnow              Abra             Absol          Accelgor 
##                 1                 1                 1                 1 
##         Aegislash        Aerodactyl            Aggron             Aipom 
##                 1                 1                 1                 1 
##          Alakazam          Alcremie         Alomomola           Altaria 
##                 1                 1                 1                 1 
##            Amaura           Ambipom         Amoonguss          Ampharos 
##                 1                 1                 1                 1 
##           Anorith          Appletun            Applin         Araquanid 
##                 1                 1                 1                 1 
##             Arbok          Arcanine            Arceus            Archen 
##                 1                 1                 1                 1 
##          Archeops         Arctovish         Arctozolt           Ariados 
##                 1                 1                 1                 1 
##           Armaldo        Aromatisse              Aron          Arrokuda 
##                 1                 1                 1                 1 
##          Articuno            Audino           Aurorus           Avalugg 
##                 1                 1                 1                 1 
##              Axew             Azelf         Azumarill           Azurill 
##                 1                 1                 1                 1 
##             Bagon            Baltoy           Banette        Barbaracle 
##                 1                 1                 1                 1 
##          Barboach       Barraskewda          Basculin         Bastiodon 
##                 1                 1                 1                 1 
##           Bayleef           Beartic         Beautifly          Beedrill 
##                 1                 1                 1                 1 
##          Beheeyem            Beldum         Bellossom        Bellsprout 
##                 1                 1                 1                 1 
##          Bergmite            Bewear           Bibarel            Bidoof 
##                 1                 1                 1                 1 
##           Binacle           Bisharp       Blacephalon         Blastoise 
##                 1                 1                 1                 1 
##          Blaziken           Blipbug           Blissey           Blitzle 
##                 1                 1                 1                 1 
##           Boldore           Boltund            Bonsly        Bouffalant 
##                 1                 1                 1                 1 
##         Bounsweet           Braixen          Braviary           Breloom 
##                 1                 1                 1                 1 
##           Brionne          Bronzong           Bronzor           Bruxish 
##                 1                 1                 1                 1 
##             Budew            Buizel         Bulbasaur           Buneary 
##                 1                 1                 1                 1 
##          Bunnelby             Burmy        Butterfree          Buzzwole 
##                 1                 1                 1                 1 
##            Cacnea          Cacturne           Calyrex          Camerupt 
##                 1                 1                 1                 1 
##           Carbink            Carkol         Carnivine        Carracosta 
##                 1                 1                 1                 1 
##          Carvanha           Cascoon          Castform          Caterpie 
##                 1                 1                 1                 1 
##            Celebi        Celesteela       Centiskorch        Chandelure 
##                 1                 1                 1                 1 
##           Chansey         Charizard         Charjabug        Charmander 
##                 1                 1                 1                 1 
##        Charmeleon            Chatot           Cherrim           Cherubi 
##                 1                 1                 1                 1 
##        Chesnaught           Chespin           Chewtle         Chikorita 
##                 1                 1                 1                 1 
##          Chimchar          Chimecho          Chinchou         Chingling 
##                 1                 1                 1                 1 
##          Cinccino         Cinderace          Clamperl         Clauncher 
##                 1                 1                 1                 1 
##         Clawitzer           Claydol          Clefable          Clefairy 
##                 1                 1                 1                 1 
##            Cleffa         Clobbopus          Cloyster         Coalossal 
##                 1                 1                 1                 1 
##          Cobalion        Cofagrigus            Combee         Combusken 
##                 1                 1                 1                 1 
##            Comfey        Conkeldurr        Copperajah          Corphish 
##                 1                 1                 1                 1 
##           Corsola       Corviknight       Corvisquire           Cosmoem 
##                 1                 1                 1                 1 
##            Cosmog          Cottonee      Crabominable        Crabrawler 
##                 1                 1                 1                 1 
##           Cradily         Cramorant          Cranidos         Crawdaunt 
##                 1                 1                 1                 1 
##         Cresselia          Croagunk            Crobat          Croconaw 
##                 1                 1                 1                 1 
##           Crustle         Cryogonal           Cubchoo            Cubone 
##                 1                 1                 1                 1 
##            Cufant           Cursola          Cutiefly         Cyndaquil 
##                 1                 1                 1                 1 
##           Darkrai        Darmanitan           Dartrix          Darumaka 
##                 1                 1                 1                 1 
##         Decidueye           Dedenne          Deerling             Deino 
##                 1                 1                 1                 1 
##          Delcatty          Delibird           Delphox            Deoxys 
##                 1                 1                 1                 1 
##           Dewgong            Dewott          Dewpider          Dhelmise 
##                 1                 1                 1                 1 
##            Dialga           Diancie         Diggersby           Diglett 
##                 1                 1                 1                 1 
##             Ditto            Dodrio             Doduo           Donphan 
##                 1                 1                 1                 1 
##           Dottler          Doublade         Dracovish         Dracozolt 
##                 1                 1                 1                 1 
##          Dragalge         Dragapult         Dragonair         Dragonite 
##                 1                 1                 1                 1 
##          Drakloak            Drampa           Drapion           Dratini 
##                 1                 1                 1                 1 
##           Drednaw            Dreepy          Drifblim          Drifloon 
##                 1                 1                 1                 1 
##           Drilbur          Drizzile           Drowzee         Druddigon 
##                 1                 1                 1                 1 
##           Dubwool          Ducklett           Dugtrio         Dunsparce 
##                 1                 1                 1                 1 
##           Duosion         Duraludon            Durant          Dusclops 
##                 1                 1                 1                 1 
##          Dusknoir           Duskull            Dustox           Dwebble 
##                 1                 1                 1                 1 
##         Eelektrik        Eelektross             Eevee            Eiscue 
##                 1                 1                 1                 1 
##             Ekans          Eldegoss        Electabuzz        Electivire 
##                 1                 1                 1                 1 
##         Electrike         Electrode            Elekid            Elgyem 
##                 1                 1                 1                 1 
##            Emboar            Emolga          Empoleon             Entei 
##                 1                 1                 1                 1 
##        Escavalier            Espeon            Espurr         Eternatus 
##                 1                 1                 1                 1 
##         Excadrill         Exeggcute         Exeggutor           Exploud 
##                 1                 1                 1                 1 
##           Falinks        Farfetch'd            Fearow            Feebas 
##                 1                 1                 1                 1 
##          Fennekin        Feraligatr         Ferroseed        Ferrothorn 
##                 1                 1                 1                 1 
##           Finneon           Flaaffy         Flabébé           Flapple 
##                 1                 1                 1                 1 
##           Flareon       Fletchinder        Fletchling          Floatzel 
##                 1                 1                 1                 1 
##           Floette           Florges            Flygon          Fomantis 
##                 1                 1                 1                 1 
##           Foongus        Forretress           Fraxure          Frillish 
##                 1                 1                 1                 1 
##           Froakie         Frogadier          Froslass          Frosmoth 
##                 1                 1                 1                 1 
##           Furfrou            Furret            Gabite           Gallade 
##                 1                 1                 1                 1 
##        Galvantula          Garbodor          Garchomp         Gardevoir 
##                 1                 1                 1                 1 
##            Gastly         Gastrodon          Genesect            Gengar 
##                 1                 1                 1                 1 
##           Geodude             Gible          Gigalith         Girafarig 
##                 1                 1                 1                 1 
##          Giratina           Glaceon            Glalie           Glameow 
##                 1                 1                 1                 1 
##         Glastrier            Gligar           Gliscor             Gloom 
##                 1                 1                 1                 1 
##            Gogoat            Golbat           Goldeen           Golduck 
##                 1                 1                 1                 1 
##             Golem            Golett         Golisopod            Golurk 
##                 1                 1                 1                 1 
##            Goodra             Goomy          Gorebyss        Gossifleur 
##                 1                 1                 1                 1 
##           Gothita        Gothitelle         Gothorita         Gourgeist 
##                 1                 1                 1                 1 
##          Granbull         Grapploct          Graveler          Greedent 
##                 1                 1                 1                 1 
##          Greninja            Grimer        Grimmsnarl           Grookey 
##                 1                 1                 1                 1 
##            Grotle           Groudon           Grovyle         Growlithe 
##                 1                 1                 1                 1 
##           Grubbin           Grumpig            Gulpin          Gumshoos 
##                 1                 1                 1                 1 
##           Gurdurr          Guzzlord          Gyarados          Hakamo-o 
##                 1                 1                 1                 1 
##           Happiny          Hariyama           Hatenna         Hatterene 
##                 1                 1                 1                 1 
##           Hattrem           Haunter          Hawlucha           Haxorus 
##                 1                 1                 1                 1 
##           Heatmor           Heatran         Heliolisk        Helioptile 
##                 1                 1                 1                 1 
##         Heracross           Herdier        Hippopotas         Hippowdon 
##                 1                 1                 1                 1 
##        Hitmonchan         Hitmonlee         Hitmontop             Ho-oh 
##                 1                 1                 1                 1 
##         Honchkrow           Honedge             Hoopa          Hoothoot 
##                 1                 1                 1                 1 
##            Hoppip            Horsea          Houndoom          Houndour 
##                 1                 1                 1                 1 
##           Huntail         Hydreigon             Hypno         Igglybuff 
##                 1                 1                 1                 1 
##          Illumise          Impidimp        Incineroar          Indeedee 
##                 1                 1                 1                 1 
##         Infernape             Inkay          Inteleon           Ivysaur 
##                 1                 1                 1                 1 
##          Jangmo-o         Jellicent        Jigglypuff           Jirachi 
##                 1                 1                 1                 1 
##           Jolteon            Joltik          Jumpluff              Jynx 
##                 1                 1                 1                 1 
##            Kabuto          Kabutops           Kadabra            Kakuna 
##                 1                 1                 1                 1 
##        Kangaskhan        Karrablast           Kartana           Kecleon 
##                 1                 1                 1                 1 
##            Keldeo           Kingdra           Kingler            Kirlia 
##                 1                 1                 1                 1 
##             Klang            Klefki             Klink         Klinklang 
##                 1                 1                 1                 1 
##           Koffing            Komala           Kommo-o            Krabby 
##                 1                 1                 1                 1 
##         Kricketot        Kricketune          Krokorok        Krookodile 
##                 1                 1                 1                 1 
##             Kubfu            Kyogre            Kyurem            Lairon 
##                 1                 1                 1                 1 
##           Lampent          Landorus           Lanturn            Lapras 
##                 1                 1                 1                 1 
##          Larvesta          Larvitar            Latias            Latios 
##                 1                 1                 1                 1 
##           Leafeon          Leavanny            Ledian            Ledyba 
##                 1                 1                 1                 1 
##        Lickilicky         Lickitung           Liepard            Lileep 
##                 1                 1                 1                 1 
##         Lilligant          Lillipup           Linoone            Litleo 
##                 1                 1                 1                 1 
##            Litten           Litwick            Lombre           Lopunny 
##                 1                 1                 1                 1 
##             Lotad           Loudred           Lucario          Ludicolo 
##                 1                 1                 1                 1 
##             Lugia          Lumineon            Lunala          Lunatone 
##                 1                 1                 1                 1 
##          Lurantis           Luvdisc             Luxio            Luxray 
##                 1                 1                 1                 1 
##          Lycanroc           Machamp           Machoke            Machop 
##                 1                 1                 1                 1 
##             Magby          Magcargo          Magearna          Magikarp 
##                 1                 1                 1                 1 
##            Magmar         Magmortar         Magnemite          Magneton 
##                 1                 1                 1                 1 
##         Magnezone          Makuhita           Malamar         Mamoswine 
##                 1                 1                 1                 1 
##           Manaphy         Mandibuzz         Manectric            Mankey 
##                 1                 1                 1                 1 
##           Mantine           Mantyke          Maractus          Mareanie 
##                 1                 1                 1                 1 
##            Mareep            Marill           Marowak         Marshadow 
##                 1                 1                 1                 1 
##         Marshtomp        Masquerain            Mawile          Medicham 
##                 1                 1                 1                 1 
##          Meditite    Mega Abomasnow        Mega Absol    Mega Aegislash 
##                 1                 1                 1                 1 
##   Mega Aerodactyl       Mega Aggron     Mega Alakazam      Mega Altaria 
##                 1                 1                 1                 1 
##     Mega Ampharos     Mega Articuno       Mega Audino      Mega Banette 
##                 1                 1                 1                 1 
##     Mega Basculin     Mega Beedrill    Mega Blastoise     Mega Blaziken 
##                 1                 1                 1                 1 
##      Mega Calyrex    Mega Calyrex X     Mega Camerupt     Mega Castform 
##                 1                 1                 1                 1 
##   Mega Castform X    Mega Charizard  Mega Charizard X      Mega Corsola 
##                 2                 1                 1                 1 
##   Mega Darmanitan Mega Darmanitan X     Mega Darumaka       Mega Deoxys 
##                 1                 2                 1                 1 
##     Mega Deoxys X      Mega Diancie      Mega Diglett      Mega Dugtrio 
##                 2                 1                 1                 1 
##        Mega Eevee       Mega Eiscue    Mega Eternatus    Mega Exeggutor 
##                 1                 1                 1                 1 
##   Mega Farfetch'd      Mega Gallade     Mega Garchomp    Mega Gardevoir 
##                 1                 1                 1                 1 
##       Mega Gengar      Mega Geodude     Mega Giratina       Mega Glalie 
##                 1                 1                 1                 1 
##        Mega Golem    Mega Gourgeist  Mega Gourgeist X     Mega Graveler 
##                 1                 1                 2                 1 
##     Mega Greninja       Mega Grimer      Mega Groudon     Mega Gyarados 
##                 1                 1                 1                 1 
##    Mega Heracross        Mega Hoopa     Mega Houndoom     Mega Indeedee 
##                 1                 1                 1                 1 
##   Mega Kangaskhan       Mega Keldeo       Mega Kyogre       Mega Kyurem 
##                 1                 1                 1                 1 
##     Mega Kyurem X     Mega Landorus       Mega Latias       Mega Latios 
##                 1                 1                 1                 1 
##      Mega Linoone      Mega Lopunny      Mega Lucario     Mega Lycanroc 
##                 1                 1                 1                 1 
##   Mega Lycanroc X    Mega Manectric      Mega Marowak       Mega Mawile 
##                 1                 1                 1                 1 
##     Mega Medicham     Mega Meloetta     Mega Meowstic       Mega Meowth 
##                 1                 1                 1                 1 
##     Mega Meowth X    Mega Metagross       Mega Mewtwo     Mega Mewtwo X 
##                 1                 1                 1                 1 
##       Mega Minior      Mega Moltres      Mega Morpeko     Mega Mr. Mime 
##                 1                 1                 1                 1 
##          Mega Muk     Mega Necrozma   Mega Necrozma X    Mega Ninetales 
##                 1                 1                 2                 1 
##     Mega Oricorio   Mega Oricorio X      Mega Persian      Mega Pidgeot 
##                 1                 2                 1                 1 
##      Mega Pikachu       Mega Pinsir       Mega Ponyta    Mega Pumpkaboo 
##                 1                 1                 1                 1 
##  Mega Pumpkaboo X       Mega Raichu     Mega Rapidash     Mega Raticate 
##                 2                 1                 1                 1 
##      Mega Rattata     Mega Rayquaza     Mega Rockruff        Mega Rotom 
##                 1                 1                 1                 1 
##      Mega Rotom X      Mega Sableye    Mega Salamence    Mega Sandshrew 
##                 4                 1                 1                 1 
##    Mega Sandslash     Mega Sceptile       Mega Scizor     Mega Sharpedo 
##                 1                 1                 1                 1 
##      Mega Shaymin      Mega Slowbro    Mega Slowbro X     Mega Slowking 
##                 1                 1                 1                 1 
##     Mega Slowpoke      Mega Steelix     Mega Stunfisk     Mega Swampert 
##                 1                 1                 1                 1 
##    Mega Thundurus     Mega Tornadus   Mega Toxtricity    Mega Tyranitar 
##                 1                 1                 1                 1 
##      Mega Urshifu     Mega Venusaur       Mega Vulpix      Mega Weezing 
##                 1                 1                 1                 1 
##   Mega Wishiwashi     Mega Wormadam   Mega Wormadam X       Mega Yamask 
##                 1                 1                 1                 1 
##       Mega Zacian    Mega Zamazenta       Mega Zapdos    Mega Zigzagoon 
##                 1                 1                 1                 1 
##      Mega Zygarde    Mega Zygarde X          Meganium          Melmetal 
##                 1                 1                 1                 1 
##          Meloetta            Meltan          Meowstic            Meowth 
##                 1                 1                 1                 1 
##           Mesprit         Metagross            Metang           Metapod 
##                 1                 1                 1                 1 
##               Mew            Mewtwo           Mienfoo          Mienshao 
##                 1                 1                 1                 1 
##         Mightyena           Milcery           Milotic           Miltank 
##                 1                 1                 1                 1 
##          Mime Jr.           Mimikyu          Minccino            Minior 
##                 1                 1                 1                 1 
##             Minun        Misdreavus         Mismagius           Moltres 
##                 1                 1                 1                 1 
##          Monferno          Morelull           Morgrem           Morpeko 
##                 1                 1                 1                 1 
##            Mothim          Mr. Mime          Mr. Rime           Mudbray 
##                 1                 1                 1                 1 
##            Mudkip          Mudsdale               Muk          Munchlax 
##                 1                 1                 1                 1 
##             Munna           Murkrow          Musharna         Naganadel 
##                 1                 1                 1                 1 
##              Natu          Necrozma            Nickit          Nidoking 
##                 1                 1                 1                 1 
##         Nidoqueen        Nidoranâ\231‚        Nidoranâ\231\200          Nidorina 
##                 1                 1                 1                 1 
##          Nidorino          Nihilego           Nincada         Ninetales 
##                 1                 1                 1                 1 
##           Ninjask           Noctowl            Noibat           Noivern 
##                 1                 1                 1                 1 
##          Nosepass             Numel           Nuzleaf         Obstagoon 
##                 1                 1                 1                 1 
##         Octillery            Oddish           Omanyte           Omastar 
##                 1                 1                 1                 1 
##              Onix          Oranguru          Orbeetle          Oricorio 
##                 1                 1                 1                 1 
##          Oshawott         Pachirisu            Palkia         Palossand 
##                 1                 1                 1                 1 
##         Palpitoad           Pancham           Pangoro           Panpour 
##                 1                 1                 1                 1 
##           Pansage           Pansear             Paras          Parasect 
##                 1                 1                 1                 1 
##         Passimian            Patrat          Pawniard          Pelipper 
##                 1                 1                 1                 1 
##        Perrserker           Persian           Petilil            Phanpy 
##                 1                 1                 1                 1 
##          Phantump         Pheromosa            Phione             Pichu 
##                 1                 1                 1                 1 
##           Pidgeot         Pidgeotto            Pidgey            Pidove 
##                 1                 1                 1                 1 
##           Pignite           Pikachu           Pikipek         Piloswine 
##                 1                 1                 1                 1 
##        Pincurchin            Pineco            Pinsir            Piplup 
##                 1                 1                 1                 1 
##            Plusle           Poipole          Politoed           Poliwag 
##                 1                 1                 1                 1 
##         Poliwhirl         Poliwrath       Polteageist            Ponyta 
##                 1                 1                 1                 1 
##         Poochyena           Popplio           Porygon         Porygon-Z 
##                 1                 1                 1                 1 
##          Porygon2         Primarina          Primeape          Prinplup 
##                 1                 1                 1                 1 
##         Probopass           Psyduck         Pumpkaboo           Pupitar 
##                 1                 1                 1                 1 
##          Purrloin           Purugly            Pyroar         Pyukumuku 
##                 1                 1                 1                 1 
##          Quagsire           Quilava         Quilladin          Qwilfish 
##                 1                 1                 1                 1 
##            Raboot            Raichu            Raikou             Ralts 
##                 1                 1                 1                 1 
##         Rampardos          Rapidash          Raticate           Rattata 
##                 1                 1                 1                 1 
##          Rayquaza            Regice         Regidrago         Regieleki 
##                 1                 1                 1                 1 
##         Regigigas          Regirock         Registeel         Relicanth 
##                 1                 1                 1                 1 
##          Remoraid          Reshiram         Reuniclus            Rhydon 
##                 1                 1                 1                 1 
##           Rhyhorn         Rhyperior          Ribombee         Rillaboom 
##                 1                 1                 1                 1 
##             Riolu          Rockruff        Roggenrola          Rolycoly 
##                 1                 1                 1                 1 
##          Rookidee           Roselia          Roserade             Rotom 
##                 1                 1                 1                 1 
##            Rowlet           Rufflet         Runerigus           Sableye 
##                 1                 1                 1                 1 
##         Salamence          Salandit          Salazzle          Samurott 
##                 1                 1                 1                 1 
##        Sandaconda           Sandile         Sandshrew         Sandslash 
##                 1                 1                 1                 1 
##         Sandygast              Sawk          Sawsbuck        Scatterbug 
##                 1                 1                 1                 1 
##          Sceptile            Scizor         Scolipede         Scorbunny 
##                 1                 1                 1                 1 
##           Scrafty           Scraggy           Scyther            Seadra 
##                 1                 1                 1                 1 
##           Seaking            Sealeo            Seedot              Seel 
##                 1                 1                 1                 1 
##        Seismitoad           Sentret         Serperior           Servine 
##                 1                 1                 1                 1 
##           Seviper          Sewaddle          Sharpedo           Shaymin 
##                 1                 1                 1                 1 
##          Shedinja           Shelgon          Shellder           Shellos 
##                 1                 1                 1                 1 
##           Shelmet          Shieldon           Shiftry         Shiinotic 
##                 1                 1                 1                 1 
##             Shinx         Shroomish           Shuckle           Shuppet 
##                 1                 1                 1                 1 
##          Sigilyph           Silcoon         Silicobra          Silvally 
##                 1                 1                 1                 1 
##          Simipour          Simisage          Simisear          Sinistea 
##                 1                 1                 1                 1 
##        Sirfetch'd        Sizzlipede          Skarmory            Skiddo 
##                 1                 1                 1                 1 
##          Skiploom            Skitty           Skorupi            Skrelp 
##                 1                 1                 1                 1 
##          Skuntank           Skwovet           Slaking           Slakoth 
##                 1                 1                 1                 1 
##           Sliggoo           Slowbro          Slowking          Slowpoke 
##                 1                 1                 1                 1 
##            Slugma          Slurpuff          Smeargle          Smoochum 
##                 1                 1                 1                 1 
##           Sneasel             Snivy              Snom           Snorlax 
##                 1                 1                 1                 1 
##           Snorunt            Snover          Snubbull            Sobble 
##                 1                 1                 1                 1 
##          Solgaleo           Solosis           Solrock           Spearow 
##                 1                 1                 1                 1 
##         Spectrier            Spewpa            Spheal          Spinarak 
##                 1                 1                 1                 1 
##            Spinda         Spiritomb            Spoink          Spritzee 
##                 1                 1                 1                 1 
##          Squirtle         Stakataka          Stantler         Staraptor 
##                 1                 1                 1                 1 
##          Staravia            Starly           Starmie            Staryu 
##                 1                 1                 1                 1 
##           Steelix           Steenee       Stonjourner         Stoutland 
##                 1                 1                 1                 1 
##           Stufful          Stunfisk            Stunky         Sudowoodo 
##                 1                 1                 1                 1 
##           Suicune          Sunflora           Sunkern           Surskit 
##                 1                 1                 1                 1 
##            Swablu          Swadloon            Swalot          Swampert 
##                 1                 1                 1                 1 
##            Swanna           Swellow            Swinub           Swirlix 
##                 1                 1                 1                 1 
##           Swoobat           Sylveon           Taillow        Talonflame 
##                 1                 1                 1                 1 
##           Tangela         Tangrowth         Tapu Bulu         Tapu Fini 
##                 1                 1                 1                 1 
##         Tapu Koko         Tapu Lele            Tauros         Teddiursa 
##                 1                 1                 1                 1 
##         Tentacool        Tentacruel             Tepig         Terrakion 
##                 1                 1                 1                 1 
##           Thievul             Throh         Thundurus          Thwackey 
##                 1                 1                 1                 1 
##           Timburr          Tirtouga        Togedemaru          Togekiss 
##                 1                 1                 1                 1 
##            Togepi           Togetic           Torchic           Torkoal 
##                 1                 1                 1                 1 
##          Tornadus          Torracat          Torterra          Totodile 
##                 1                 1                 1                 1 
##         Toucannon           Toxapex             Toxel         Toxicroak 
##                 1                 1                 1                 1 
##        Toxtricity         Tranquill          Trapinch           Treecko 
##                 1                 1                 1                 1 
##         Trevenant           Tropius          Trubbish          Trumbeak 
##                 1                 1                 1                 1 
##          Tsareena        Turtonator           Turtwig           Tympole 
##                 1                 1                 1                 1 
##            Tynamo        Type: Null        Typhlosion         Tyranitar 
##                 1                 1                 1                 1 
##         Tyrantrum           Tyrogue            Tyrunt           Umbreon 
##                 1                 1                 1                 1 
##          Unfezant             Unown          Ursaring           Urshifu 
##                 1                 1                 1                 1 
##              Uxie         Vanillish         Vanillite         Vanilluxe 
##                 1                 1                 1                 1 
##          Vaporeon          Venipede          Venomoth           Venonat 
##                 1                 1                 1                 1 
##          Venusaur         Vespiquen           Vibrava           Victini 
##                 1                 1                 1                 1 
##        Victreebel          Vigoroth          Vikavolt         Vileplume 
##                 1                 1                 1                 1 
##          Virizion          Vivillon           Volbeat         Volcanion 
##                 1                 1                 1                 1 
##         Volcarona           Voltorb           Vullaby            Vulpix 
##                 1                 1                 1                 1 
##           Wailmer           Wailord           Walrein         Wartortle 
##                 1                 1                 1                 1 
##           Watchog           Weavile            Weedle        Weepinbell 
##                 1                 1                 1                 1 
##           Weezing        Whimsicott        Whirlipede          Whiscash 
##                 1                 1                 1                 1 
##           Whismur        Wigglytuff            Wimpod           Wingull 
##                 1                 1                 1                 1 
##        Wishiwashi         Wobbuffet            Woobat            Wooloo 
##                 1                 1                 1                 1 
##            Wooper          Wormadam           Wurmple            Wynaut 
##                 1                 1                 1                 1 
##              Xatu           Xerneas         Xurkitree            Yamask 
##                 1                 1                 1                 1 
##            Yamper             Yanma           Yanmega           Yungoos 
##                 1                 1                 1                 1 
##           Yveltal            Zacian         Zamazenta          Zangoose 
##                 1                 1                 1                 1 
##            Zapdos            Zarude         Zebstrika            Zekrom 
##                 1                 1                 1                 1 
##           Zeraora         Zigzagoon           Zoroark             Zorua 
##                 1                 1                 1                 1 
##             Zubat          Zweilous           Zygarde 
##                 1                 1                 1 
## 
## $Total
## NULL
## 
## $HP
## NULL
## 
## $Attack
## NULL
## 
## $Defence
## NULL
## 
## $Sp_attack
## NULL
d.df=data.frame(d2$HP,d2$Attack)
head(d.df)

Summarize using a grouping variable:

head(aggregate(d2[2:5], by = list(d2$Attack), FUN = sum))
head(aggregate(d2$Attack ~ d2$Defence, data = d2, FUN = mean))
head(aggregate(cbind(d2$Attack, d2$Defence) ~ d2$Sp_attack, data = d2, FUN = mean))
head(aggregate(. ~ d2$Defence, data = d2, FUN = mean))
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
pw.agg = aggregate(d2$Attack ~ d2$Defence * d2$Sp_attack * d2$HP, data = d2, FUN = mean)
head(pw.agg)
head(aggregate(d2$Attack ~ . , data = d2, FUN = mean))
head(aggregate(d2$HP ~ 1 , data = d2, FUN = mean))

Curvilinear regression:

lm(d2$HP ~ d2$Attack, data = d2)
## 
## Call:
## lm(formula = d2$HP ~ d2$Attack, data = d2)
## 
## Coefficients:
## (Intercept)    d2$Attack  
##     40.6616       0.3654
fw.lm = lm(d2$HP ~ d2$Attack, data = d2)
summary(fw.lm)
## 
## Call:
## lm(formula = d2$HP ~ d2$Attack, data = d2)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -72.552 -14.033  -3.070   8.757 210.684 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 40.66163    1.98019   20.53   <2e-16 ***
## d2$Attack    0.36545    0.02283   16.01   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 23.91 on 1043 degrees of freedom
## Multiple R-squared:  0.1972, Adjusted R-squared:  0.1965 
## F-statistic: 256.3 on 1 and 1043 DF,  p-value: < 2.2e-16
fw.lm$rank
## [1] 2
coef(fw.lm)
## (Intercept)   d2$Attack 
##  40.6616348   0.3654456
 confint(fw.lm)
##                  2.5 %     97.5 %
## (Intercept) 36.7760168 44.5472528
## d2$Attack    0.3206517  0.4102395
 confint(fw.lm, parm = c('(Intercept)', 'HP'), level = 0.9)
##                  5 %     95 %
## (Intercept) 37.40161 43.92166
## HP                NA       NA
head(fitted(fw.lm))
##        1        2        3        4        5        6 
## 58.56847 63.31926 70.62818 77.20620 59.66481 64.05015
head(residuals(fw.lm))
##          1          2          3          4          5          6 
## -13.568470  -3.319263   9.371824   2.793803 -20.664807  -6.050155
formula(fw.lm)
## d2$HP ~ d2$Attack
fw.lm$cal
## lm(formula = d2$HP ~ d2$Attack, data = d2)
plot(d2$Attack, d2$HP)

plot(~ d2$Attack + d2$Defence, data = d2)

plot(d2$Attack ~ d2$Sp_defence, data = d2)
abline(lm(d2$HP ~ d2$Attack, data = d2))
abline(a = coef(fw.lm[1], b = coef(fw.lm[2])))
abline(coef(fw.lm))

anova(fw.lm)
mf.lm = lm(d2$HP ~ d2$Attack, data = d2)
mf.lm
## 
## Call:
## lm(formula = d2$HP ~ d2$Attack, data = d2)
## 
## Coefficients:
## (Intercept)    d2$Attack  
##     40.6616       0.3654
pb.lm = lm(d2$HP ~ d2$Attack, data = d2)
pb.lm
## 
## Call:
## lm(formula = d2$HP ~ d2$Attack, data = d2)
## 
## Coefficients:
## (Intercept)    d2$Attack  
##     40.6616       0.3654
mf.lm = lm(d2$HP ~ 1, data = d2)
pb.lm = lm(d2$HP ~ 1, data = d2)
mf.lm
## 
## Call:
## lm(formula = d2$HP ~ 1, data = d2)
## 
## Coefficients:
## (Intercept)  
##       70.07
pb.lm
## 
## Call:
## lm(formula = d2$HP ~ 1, data = d2)
## 
## Coefficients:
## (Intercept)  
##       70.07
add1(mf.lm, scope = d2)
mf.lm = lm(d2$HP ~ d2$Attack, data = d2)
mf.lm
## 
## Call:
## lm(formula = d2$HP ~ d2$Attack, data = d2)
## 
## Coefficients:
## (Intercept)    d2$Attack  
##     40.6616       0.3654
mf.lm = lm(d2$HP ~ d2$Attack + d2$Defence, data = d2)
summary(mf.lm)
## 
## Call:
## lm(formula = d2$HP ~ d2$Attack + d2$Defence, data = d2)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -69.002 -13.623  -2.984   8.762 214.203 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 36.55648    2.22273  16.447  < 2e-16 ***
## d2$Attack    0.31922    0.02550  12.521  < 2e-16 ***
## d2$Defence   0.10480    0.02646   3.962 7.95e-05 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 23.74 on 1042 degrees of freedom
## Multiple R-squared:  0.2092, Adjusted R-squared:  0.2076 
## F-statistic: 137.8 on 2 and 1042 DF,  p-value: < 2.2e-16
mf.lm = lm(d2$HP ~ d2$Attack, data = d2)
mf.lm
## 
## Call:
## lm(formula = d2$HP ~ d2$Attack, data = d2)
## 
## Coefficients:
## (Intercept)    d2$Attack  
##     40.6616       0.3654
mf.lm1 = lm(d2$Attack ~ d2$HP, data = d2)
mf.lm2 = lm(d2$Attack ~ ., data = d2)
anova(mf.lm1,mf.lm2)
pg.lm = lm(d2$HP ~ log(d2$Attack), data = d2)
summary(pg.lm)
## 
## Call:
## lm(formula = d2$HP ~ log(d2$Attack), data = d2)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -73.750 -14.750  -3.439   8.813 241.564 
## 
## Coefficients:
##                Estimate Std. Error t value Pr(>|t|)    
## (Intercept)     -28.490      7.158   -3.98 7.36e-05 ***
## log(d2$Attack)   22.943      1.657   13.85  < 2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 24.52 on 1043 degrees of freedom
## Multiple R-squared:  0.1553, Adjusted R-squared:  0.1545 
## F-statistic: 191.7 on 1 and 1043 DF,  p-value: < 2.2e-16
 bbel.lm = lm(d2$HP ~ d2$Attack + I(Attack^2), data = d2)
 bbel.lm
## 
## Call:
## lm(formula = d2$HP ~ d2$Attack + I(Attack^2), data = d2)
## 
## Coefficients:
## (Intercept)    d2$Attack  I(Attack^2)  
##   33.823443     0.545988    -0.001022
plot(d2$Attack ~ d2$Defence, data = d2)

plot(d2$Attack ~ d2$Sp_attack, data = d2)
lines(d2$Attack, fitted(pg.lm))
lines(d2$Defence, fitted(bbel.lm))

head(predict(mf.lm))
##        1        2        3        4        5        6 
## 58.56847 63.31926 70.62818 77.20620 59.66481 64.05015
prd = predict(mf.lm, interval = 'confidence', level = 0.95)

attach(d2)
## The following objects are masked from head(d2):
## 
##     Attack, Defence, HP, Name, Sp_attack, Sp_defence, Speed, Total
prd = data.frame(prd, d2)
detach(d2)

prd = data.frame(prd)
prd$d2 = d2$d2
head(prd)
plot(d2$HP ~ d2$Attack, data =d2) # basic plot
lines(prd$HP, prd$fit) # also best fit
lines(prd$Attack, prd$lwr, lty = 2) # lower CI
lines(prd$Defence, prd$upr, lty = 2) # upper CI

mf.lm = lm(d2$Attack ~ d2$Defence + d2$Sp_attack, data = d2)
plot(mf.lm)

bf.m = apply(d2, 2, mean, na.rm = TRUE)
## Warning in mean.default(newX[, i], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(newX[, i], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(newX[, i], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(newX[, i], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(newX[, i], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(newX[, i], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(newX[, i], ...): argument is not numeric or logical:
## returning NA

## Warning in mean.default(newX[, i], ...): argument is not numeric or logical:
## returning NA
bf.sd = apply(d2, 2, sd, na.rm = TRUE)
## Warning in var(if (is.vector(x) || is.factor(x)) x else as.double(x), na.rm =
## na.rm): NAs introduced by coercion

Bar Graphs Using ggplot() with geom_col()

library(gcookbook)
library(dplyr)
## 
## Attaching package: 'dplyr'
## The following objects are masked from 'package:stats':
## 
##     filter, lag
## The following objects are masked from 'package:base':
## 
##     intersect, setdiff, setequal, union
library(ggplot2)
ggplot(d2, aes(x = d2$Defence, y = d2$Sp_attack)) + geom_col()
## Warning: Use of `d2$Defence` is discouraged. Use `Defence` instead.
## Warning: Use of `d2$Sp_attack` is discouraged. Use `Sp_attack` instead.

ggplot(d2, aes(x = d2$HP, y = d2$Attack)) + geom_col(fill = "yellow", colour = "black")
## Warning: Use of `d2$HP` is discouraged. Use `HP` instead.
## Warning: Use of `d2$Attack` is discouraged. Use `Attack` instead.

ggplot(d2, aes(x = d2$HP, y =d2$Attack, fill = HP)) + geom_col(position = "dodge")
## Warning: Use of `d2$HP` is discouraged. Use `HP` instead.

## Warning: Use of `d2$Attack` is discouraged. Use `Attack` instead.

Bar Graphs Using ggplot() with geom_bar()

ggplot(d2, aes(x = Attack)) + geom_bar()

Using Colors in a Bar Graph:

a2 <- d2 %>% arrange(desc(Attack)) %>% slice(1:10)
a2

Adjusting Bar Width and Spacing:

ggplot(d2, aes(x = Attack, y = Defence)) + geom_col(width = 0.5)

ggplot(d2, aes(x = Attack, y = HP)) + geom_col(width = 1)

ggplot(d2, aes(x = Attack, y = HP, fill = Attack)) + geom_col(width = 0.5, position = "dodge")

ggplot(d2, aes(x = Attack, y =HP, fill = Attack)) +geom_col(width = 0.5, position = position_dodge(0.7))

ggplot(d2, aes(x = Attack, y = HP, fill = Attack)) +geom_col() + guides(fill = guide_legend(reverse = TRUE))

ggplot(d2, aes(x = Attack, y = HP, fill = Attack)) + geom_col(position = position_stack(reverse = TRUE)) + guides(fill = guide_legend(reverse = TRUE))

ggplot(d2, aes(x = Attack, y = HP, fill = Attack)) + geom_col(position = "fill") + scale_y_continuous(labels = scales::percent)

# using group_by command:
a2 <- d2 %>%group_by(Attack) %>% mutate(Sp_attack = Attack / sum(Attack) * 100)
head(a2)
ggplot(d2, aes(x = Attack, y =Sp_attack , fill = Attack)) + geom_col()

ggplot(d2, aes(x = interaction(Attack, HP), y = Defence)) +geom_col() + geom_text(aes(label = Defence), vjust = 1.5, colour = "white")